text:
\section{Introduction}
\label{sec:introduction}

Complex supervised learning algorithms, such as neural networks (NNs)
and support vector machines (SVMs), are more common than ever in
predictive analytics, especially when dealing with large observational
databases that don't adhere to the strict assumptions imposed by
traditional statistical techniques (e.g., multiple linear regression
which typically assumes linearity, homoscedasticity, and normality).
However, it can be challenging to understand the results of such complex
models and explain them to management. Graphical displays such as
variable importance plots (when available) and partial dependence plots
(PDPs) \citep{friedman-2001-greedy} offer a simple solution \citep[see,
for example,][pp.~367--380]{hastie-elements-2009}. PDPs are
low-dimensional graphical renderings of the prediction function
\(\widehat{f}\left(\boldsymbol{x}\right)\) that allow analysts to more
easily understand the estimated relationship between the outcome and
predictors of interest. These plots are especially useful in
interpreting the output from ``black box'' models. While PDPs can be
constructed for any predictor in a fitted model, variable importance
scores are more difficult to define, and when available, their
interpretation often depends on the model fitting algorithm used.

In this paper, we consider a standardized method to computing variable
importance scores using PDPs. There are a number of advantages to using
our approach. First, it offers a standardized procedure to quantifying
variable importance across the growing spectrum of supervised learning
algorithms. For example, while popular statistical learning algorithms
like random forests (RFs) and gradient boosted decision trees (GBMs)
have there own natural way of measuring variable importance, each is
interpreted differently (these are briefly described in Section
\ref{sec:model-based-vi}). Secondly, our method is suitable for use with
any trained supervised learning algorithm, provided predictions on new
data can be obtained. For example, it is often beneficial (from an
accuracy standpoint) to train and tune multiple state-of-the art
predictive models (e.g., multiple RFs, GBMs, and deep learning NNs
(DNNs)) and then combine them into an ensemble called a \emph{super
learner} through a process called \emph{model stacking}. Even if the
base learners can provide there own measures of variable importance,
there is no logical way to combine them to form an overall score for the
super learner. However, since new predictions can be obtained from the
super learner, our proposed variable importance measure is still
applicable (examples are given in Sections
\ref{sec:ensemble}--\ref{sec:automl}). Thirdly, as shown in Section
\ref{sec:interaction}, our proposed method can be modified to quantify
the strength of potential interaction effects. Finally, since our
approach is based on constructing PDPs for all the main effects, the
analyst is forced to also look at the estimated functional relationship
between each feature and the target---which should be done in tandem
with studying the importance of each feature.

\section{Background}
\label{sec:background}

We are often confronted with the task of extracting knowledge from large
databases. For this task we turn to various statistical learning
algorithms which, when tuned correctly, can have state-of-the-art
predictive performance. However, having a model that predicts well is
only solving part of the problem. It is also desirable to extract
information about the relationships uncovered by the learning algorithm.
For instance, we often want to know which predictors, if any, are
important by assigning some type of variable importance score to each
feature. Once a set of influential features has been identified, the
next step is summarizing the functional relationship between each
feature, or subset thereof, and the outcome of interest. However, since
most statistical learning algorithms are ``black box'' models,
extracting this information is not always straightforward. Luckily, some
learning algorithms have a natural way of defining variable importance.

\subsection{Model-based approaches to variable importance}
\label{sec:model-based-vi}

Decision trees probably offer the most natural model-based approach to
quantifying the importance of each feature. In a binary decision tree,
at each node \(t\), a single predictor is used to partition the data
into two homogeneous groups. The chosen predictor is the one that
maximizes some measure of improvement \(\widehat{i}_t\). The relative
importance of predictor \(x\) is the sum of the squared improvements
over all internal nodes of the tree for which \(x\) was chosen as the
partitioning variable; see \citet{classification-breiman-1984} for
details. This idea also extends to ensembles of decision trees, such as
RFs and GBMs. In ensembles, the improvement score for each predictor is
averaged across all the trees in the ensemble. Fortunately, due to the
stabilizing effect of averaging, the improvement-based variable
importance metric is often more reliable in large ensembles
\citep[see][pg. 368]{hastie-elements-2009}. RFs offer an additional
method for computing variable importance scores. The idea is to use the
leftover out-of-bag (OOB) data to construct validation-set errors for
each tree. Then, each predictor is randomly shuffled in the OOB data and
the error is computed again. The idea is that if variable \(x\) is
important, then the validation error will go up when \(x\) is perturbed
in the OOB data. The difference in the two errors is recorded for the
OOB data then averaged across all trees in the forest.

In multiple linear regression, the absolute value of the \(t\)-statistic
is commonly used as a measure of variable importance. The same idea also
extends to generalized linear models (GLMs). Multivariate adaptive
regression splines (MARS), which were introduced in
\citet{friedman-1991-mars}, is an automatic regression technique which
can be seen as a generalization of multiple linear regression and
generalized linear models. In the MARS algorithm, the contribution (or
variable importance score) for each predictor is determined using a
generalized cross-validation (GCV) statistic.

For NNs, two popular methods for constructing variable importance scores
are the Garson algorithm \citep{interpreting-garson-1991}, later
modified by \citet{back-goh-1995}, and the Olden algorithm
\citep{accurate-olden-2004}. For both algorithms, the basis of these
importance scores is the network's connection weights. The Garson
algorithm determines variable importance by identifying all weighted
connections between the nodes of interest. Olden's algorithm, on the
other hand, uses the product of the raw connection weights between each
input and output neuron and sums the product across all hidden neurons.
This has been shown to outperform the Garson method in various
simulations. For DNNs, a similar method due to \citet{data-gedeon-1997}
considers the weights connecting the input features to the first two
hidden layers (for simplicity and speed); but this method can be slow
for large networks.

\subsection{Filter-based approaches to variable importance}

Filter-based approaches, which are described in
\citet{applied-kuhn-2013}, do not make use of the fitted model to
measure variable importance. They also do not take into account the
other predictors in the model.

For regression problems, a popular approach to measuring the variable
importance of a numeric predictor \(x\) is to first fit a flexible
nonparametric model between \(x\) and the target \(Y\); for example, the
locally-weighted polynomial regression (LOWESS) method developed by
\citet{robust-cleveland-1979}. From this fit, a pseudo-\(R^2\) measure
can be obtained from the resulting residuals and used as a measure of
variable importance. For categorical predictors, a different method
based on standard statistical tests (e.g., \(t\)-tests and ANOVAs) is
employed; see \citet{applied-kuhn-2013} for details.

For classification problems, an area under the ROC curve (AUC) statistic
can be used to quantify predictor importance. The AUC statistic is
computed by using the predictor \(x\) as input to the ROC curve. If
\(x\) can reasonably separate the classes of \(Y\), that is a clear
indicator that \(x\) is an important predictor (in terms of class
separation) and this is captured in the corresponding AUC statistic. For
problems with more than two classes, extensions of the ROC curve or a
one-vs-all approach can be used.

\subsection{Partial dependence plots}

\citet{harrison-1978-hedonic} analyzed a data set containing suburban
Boston housing data from the 1970 census. They sought a housing value
equation using an assortment of features; see Table IV of
\citet{harrison-1978-hedonic} for a description of each variable. The
usual regression assumptions, such as normality, linearity, and constant
variance, were clearly violated, but through an exhausting series of
transformations, significance testing, and grid searches, they were able
to build a model which fit the data reasonably well (\(R^2 = 0.81\)).
Their prediction equation is given in Equation \eqref{eqn:boston}. This
equation makes interpreting the model easier. For example, the average
number of rooms per dwelling (\(RM\)) is included in the model as a
quadratic term with a positive coefficient. This means that there is a
monotonic increasing relationship between \(RM\) and the predicted
median home value, but larger values of \(RM\) have a greater impact.

\begin{equation}
\label{eqn:boston}
\begin{aligned}
\widehat{\log\left(MV\right)} &= 9.76 + 0.0063 RM^2 + 8.98\times10^{-5} AGE - 0.19\log\left(DIS\right) + 0.096\log\left(RAD\right) \\
  & \quad - 4.20\times10^{-4} TAX - 0.031 PTRATIO + 0.36\left(B - 0.63\right)^2 - 0.37\log\left(LSTAT\right) \\
  & \quad - 0.012 CRIM + 8.03\times10^{-5} ZN + 2.41\times10^{-4} INDUS + 0.088 CHAS \\
  & \quad - 0.0064 NOX^2.
\end{aligned}
\end{equation}

However, classical regression and model building is rather ill-suited
for more contemporary big data sets, like the Ames housing data
described in \citet{ames-cock-2011} which has a total of 79 predictors
(and many more that can be created through feature engineering).
Fortunately, using modern computing power, many supervised learning
algorithms can fit such data sets in seconds, producing powerful, highly
accurate models. The downfall of many of these machine learning
algorithms, however, is decreased interpretability. For example, fitting
a well-tuned RF to the Boston housing data will likely produce a model
with more accurate predictions, but no interpretable prediction formula
such as the one in Equation \eqref{eqn:boston}.

To help understand the estimated functional relationship between each
predictor and the outcome of interest in a fitted model, we can
construct PDPs. PDPs are particularly effective at helping to explain
the output from ``black box'' models, such as RFs and SVMs. Not only do
PDPs visually convey the relationship between low cardinality subsets of
the feature set (usually 1-3) and the response (while accounting for the
average effect of the other predictors in the model), they can also be
used to rank and score the predictors in terms of their relative
influence on the predicted outcome, as will be demonstrated in this
paper.

Let \(\boldsymbol{x} = \left\{x_1, x_2, \dots, x_p\right\}\) represent
the predictors in a model whose prediction function is
\(\widehat{f}\left(\boldsymbol{x}\right)\). If we partition
\(\boldsymbol{x}\) into an interest set, \(\boldsymbol{z}_s\), and its
complement,
\(\boldsymbol{z}_c = \boldsymbol{x} \setminus \boldsymbol{z}_s\), then
the ``partial dependence'' of the response on \(\boldsymbol{z}_s\) is
defined as

\begin{equation}
\label{eqn:avg_fun}
  f_s\left(\boldsymbol{z}_s\right) = E_{\boldsymbol{z}_c}\left[\widehat{f}\left(\boldsymbol{z}_s, \boldsymbol{z}_c\right)\right] = \int \widehat{f}\left(\boldsymbol{z}_s, \boldsymbol{z}_c\right)p_{c}\left(\boldsymbol{z}_c\right)d\boldsymbol{z}_c,
\end{equation}

where \(p_{c}\left(\boldsymbol{z}_c\right)\) is the marginal probability
density of \(\boldsymbol{z}_c\):
\(p_{c}\left(\boldsymbol{z}_c\right) = \int p\left(\boldsymbol{x}\right)d\boldsymbol{z}_s\).
Equation \eqref{eqn:avg_fun} can be estimated from a set of training
data by

\begin{equation}
\label{eqn:pdf}
\bar{f}_s\left(\boldsymbol{z}_s\right) = \frac{1}{n}\sum_{i = 1}^n\widehat{f}\left(\boldsymbol{z}_s,\boldsymbol{z}_{i, c}\right),
\end{equation}

where \(\boldsymbol{z}_{i, c}\) \(\left(i = 1, 2, \dots, n\right)\) are
the values of \(\boldsymbol{z}_c\) that occur in the training sample;
that is, we average out the effects of all the other predictors in the
model.

Constructing a PDP \eqref{eqn:pdf} in practice is rather
straightforward. To simplify, let \(\boldsymbol{z}_s = x_1\) be the
predictor variable of interest with unique values
\(\left\{x_{11}, x_{12}, \dots, x_{1k}\right\}\). The partial dependence
of the response on \(x_1\) can be constructed as follows:

\begin{algorithm}
  \textbf{Input}: the unique predictor values $x_{11}, x_{12}, \dots, x_{1k}$;

  \textbf{Output}: the estimated partial dependence values $\bar{f}_1\left(x_{11}\right), \bar{f}_1\left(x_{12}\right), \dots, \bar{f}_1\left(x_{1k}\right)$. \BlankLine
  \For{$i \in \left\{1, 2, \dots, k\right\}$}{
    \BlankLine
    (1) copy the training data and replace the original values of $x_1$ with the constant $x_{1i}$;
    \BlankLine
    (2) compute the vector of predicted values from the modified copy of the training data;
    \BlankLine
    (3) compute the average prediction to obtain $\bar{f}_1\left(x_{1i}\right)$.\BlankLine}
    \BlankLine
    The PDP for $x_1$ is obtained by plotting the pairs $\left\{x_{1i}, \bar{f}_1\left(x_{1i}\right)\right\}$ for $i = 1, 2, \dotsc, k$.
    \BlankLine
    \BlankLine
  \caption{A simple algorithm for constructing the partial dependence of the response on a single predictor $x_1$. \label{alg:pdp}}
\end{algorithm}

Algorithm \ref{alg:pdp} can be computationally expensive since it
involves \(k\) passes over the training records. Fortunately, it is
embarrassingly parallel and computing partial dependence functions for
each predictor can be done rather quickly on a machine with a multi-core
processor. For large data sets, it may be worthwhile to reduce the grid
size by using specific quantiles for each predictor, rather than all the
unique values. For example, the partial dependence function can be
approximated very quickly by using the deciles of the unique predictor
values. The exception is classification and regression trees based on
single-variable splits which can make use of the efficient weighted tree
traversal method described in \citet{friedman-2001-greedy}.

While PDPs are an invaluable tool in understanding the relationships
uncovered by complex nonparametric models, they can be misleading in the
presence of substantial interaction effects
\citep{goldstein-peeking-2015}. To overcome this issue,
\citeauthor{goldstein-peeking-2015} introduced the concept of individual
conditional expectation (ICE) curves. ICE curves display the estimated
relationship between the response and a predictor of interest for each
observation; in other words, skipping step 1 (c) in Algorithm
\ref{alg:pdp}. Consequently, the PDP for a predictor of interest can be
obtained by averaging the corresponding ICE curves across all
observations. Although ICE curves provide a refinement over traditional
PDPs in the presence of substantial interaction effects, in Section
\ref{sec:interaction}, we show how to use partial dependence functions
to evaluate the strength of potential interaction effects.

\subsection{The Ames housing data set}
\label{sec:ames}

For illustration, we will use the Ames housing data set---a modernized
and expanded version of the often cited Boston Housing data set. These
data are available in the \texttt{AmesHousing} package
\citep{AmesHousing-pkg}. Using the R package \texttt{h2o}
\citep{h2o-pkg}, we trained and tuned a GBM using 10-fold
cross-validation. The model fit is reasonable, with a cross-validated
(pseudo) \(R^2\) of 91.54\%. Like other tree-based ensembles, GBMs have
a natural way of defining variable importance which was described in
Section \ref{sec:model-based-vi}. The variable importance scores for
these data are displayed in the left side of Figure
\ref{fig:ames-gbm-vip}. This plot indicates that the overall quality of
the material and finish of the house (\texttt{Overall\_Qual}), physical
location within the Ames city limits (\texttt{Neighborhood}), and the
above grade (ground) living area (\texttt{Gr\_Liv\_Area}) are highly
associated with the logarithm of the sales price
(\texttt{Log\_Sale\_Price}). The variable importance scores also
indicate that pool quality (\texttt{Pool\_QC}), the number of kitchens
above grade (\texttt{Kitchen\_AbvGr}), and the low quality finished
square feet for all floors (\texttt{Low\_Qual\_Fin\_SF}) have little
association with \texttt{Log\_Sale\_Price}. (Note that the bottom two
features in Figure \ref{fig:ames-gbm-vip}---\texttt{Street} and
\texttt{Utilities}---have a variable importance score of exactly zero;
in other words, they were never used to partion the data at any point in
the GBM ensemble.)

\begin{figure}

{\centering \includegraphics{figures/ames-gbm-vip-1}

}

\caption{Variable importance scores for the Ames housing data. \textit{Left:} Traditional approach. \textit{Right:} Partial dependence-based approach.}\label{fig:ames-gbm-vip}
\end{figure}

The PDPs for these six variables are displayed in Figure
\ref{fig:ames-gbm-pdps}. These plots indicate that
\texttt{Overall\_Qual}, \texttt{Neighborhood}, and
\texttt{Gr\_Liv\_Area} have a strong nonlinear relationship with the
predicted outcome. For instance, it seems that \texttt{GrLivArea} has a
monotonically increasing relationship with \texttt{Log\_Sale\_Price}
until about 12 sq ft, after which the relationship flattens out. Notice
how the PDPs for \texttt{Pool\_QC}, \texttt{Kitchen\_AbvGr}, and
\texttt{Low\_Qual\_Fin\_SF} are relatively flat in comparison. It is
this notion of ``flatness'' which we will use as a basis to define our
variable importance measure.

\begin{figure}

{\centering \includegraphics{figures/ames-gbm-pdps-1}

}

\caption{Partial dependence of log selling price on the three highest (top row) and lowest (bottom row) ranked predictors. The dashed red line in each plot represents the mean of the $N = 1460$ log selling prices}\label{fig:ames-gbm-pdps}
\end{figure}

\section{A partial dependence-based variable importance measure}
\label{sec:new}

The PDPs for \texttt{Pool\_QC}, \texttt{Kitchen\_AbvGr}, and
\texttt{Low\_Qual\_Fin\_SF} in Figure \ref{fig:ames-gbm-pdps} are
relatively flat, indicating that they do not have much influence on the
predicted value of \texttt{Log\_Sale\_Price}. In other words, the
partial dependence values \(\bar{f}_i\left(x_{ij}\right)\)
\(\left(j = 1, 2, \dots, k_i\right)\) display little variability. One
might conclude that any variable for which the PDP is ``flat'' is likely
to be less important than those predictors whose PDP varies across a
wider range of the response.

Our notion of variable importance is based on any measure of the
``flatness'' of the partial dependence function. In general, we define
\[
  i\left(x\right) = \digamma\left(\bar{f}_s\left(\boldsymbol{z}_s\right)\right),
\] where \(\digamma\left(\cdot\right)\) is any measure of the
``flatness'' of \(\bar{f}_s\left(\boldsymbol{z}_s\right)\). A simple and
effective measure to use is the sample standard deviation for continuous
predictors and the range statistic divided by four for factors with
\(K\) levels; the range divided by four provides an estimate of the
standard deviation for small to moderate sample sizes. Based on
Algorithm \ref{alg:pdp}, our importance measure for predictor \(x_1\) is
simply

\begin{equation}
\label{eqn:vi}
  i\left(x_1\right) =
  \begin{cases}
    \sqrt{\frac{1}{k - 1}\sum_{i = 1}^k\left[\bar{f}_1\left(x_{1i}\right) - \frac{1}{k}\sum_{i = 1}^k\bar{f}_1\left(x_{1i}\right)\right] ^ 2} & \quad \text{if } x_1 \text{ is continuous}\\
    \left[\max_i\left(\bar{f}_1\left(x_{1i}\right)\right) - \min_i\left(\bar{f}_1\left(x_{1i}\right)\right)\right] / 4 & \quad \text{if } x_1 \text{ is categorical}
  \end{cases}.
\end{equation}

Note that our variable importance metric relies on the fitted model;
hence, it is crucial to properly tune and train the model to attain the
best performance possible.

To illustrate, we applied Algorithm \ref{alg:pdp} to all of the
predictors in the Ames GBM model and computed \eqref{eqn:vi}. The
results are displayed in right side of Figure \ref{fig:ames-gbm-vip}. In
this case, our partial dependence-based algorithm matches closely with
the results from the GBM. In particular, Figure
\ref{fig:ames-gbm-vi-both} indicates that \texttt{Overall\_Qual},
\texttt{Neighborhood}, and \texttt{Gr\_Liv\_Area} are still the most
important variables in predicting \texttt{Log\_Sale\_Price}; though,
\texttt{Neighborhood} and \texttt{Gr\_Liv\_Area} have swapped places.

\subsection{Linear models}
\label{sec:linear}

As mentioned earlier, a natural choice for measuring the importance of
each term in a linear model is to use the absolute value of the
corresponding coefficient divided by its estimated standard error (i.e.,
the absolute value of the \(t\)-statistic). This turns out to be
equivalent to the partial dependence-based metric \eqref{eqn:vi} when
the predictors are independently and uniformly distributed over the same
range.

For example, suppose we have a linear model of the form

\begin{equation*}
  Y = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + \epsilon,
\end{equation*}

where \(\beta_i\) (\(i = 1, 2\)) is a constant, \(X_1\) and \(X_2\) are
both independent \(\mathcal{U}\left(0, 1\right)\) random variables, and
\(\epsilon \sim \mathcal{N}\left(0, \sigma ^ 2\right)\). Since we know
the distribution of \(X_1\) and \(X_2\), we can easily find
\(f_1\left(X_1\right)\) and \(f_2\left(X_2\right)\). For instance,

\begin{equation*}
  f_1\left(X_1\right) = \int_0^1 E\left[Y | X_1, X_2\right] p\left(X_2\right) dX_2,
\end{equation*}

where \(p\left(X_2\right) = 1\). Simple calculus then leads to

\begin{equation*}
  f_1\left(X_1\right) = \beta_0 + \beta_2 / 2 + \beta_1 X_1 \quad and \quad f_2\left(X_2\right) = \beta_0 + \beta_1 / 2 + \beta_2 X_2..
\end{equation*}

Because \(E\left[Y | X_1, X_2\right] = f\left(X_1, X_2\right)\) is
additive, the true partial dependence functions are just simple linear
regressions in each predictor with their original coefficient and an
adjusted intercept. Taking the variance of each gives

\begin{equation*}
Var\left[f_1\left(X_1\right)\right] = \beta_1 ^ 2 / 12 \quad and \quad Var\left[f_2\left(X_2\right)\right] = \beta_2 ^ 2 / 12.
\end{equation*}

Hence, the standard deviations are just the absolute values of the
original coefficients (scaled by the same constant).

To illustrate, we simulated \(n = 1000\) observations from the following
linear model

\begin{equation*}
  Y = 1 + 3 X_1 - 5 X_2 + \epsilon,
\end{equation*}

where \(X_1\) and \(X_2\) are both independent
\(\mathcal{U}\left(0, 1\right)\) random variables, and
\(\epsilon \sim \mathcal{N}\left(0, 0.01 ^ 2\right)\). For this example,
we have

\begin{equation*}
  f_1\left(X_1\right) = -\frac{3}{2} + 3 X_1 \quad and \quad f_2\left(X_1\right) = \frac{5}{2} - 5 X_2.
\end{equation*}

These are plotted as red lines in Figure \ref{fig:lm-pdps}.
Additionally, the black lines in Figure \ref{fig:lm-pdps} correspond to
the estimated partial dependence functions using Algorithm
\ref{alg:pdp}.

\begin{figure}

{\centering \includegraphics[width=1.0\linewidth]{figures/lm-pdps-1}

}

\caption{Estimated (black) and true (red) partial dependence functions from a linear model with two predictors.}\label{fig:lm-pdps}
\end{figure}

Based on these plots, \(X_2\) is more influential than \(X_1\). Taking
the absolute value of the ratio of the slopes in \(f_2\left(X_2\right)\)
and \(f_1\left(X_1\right)\) gives \(5 / 3 \approx 1.67\). In other
words, \(X_2\) is roughly 1.67 times more influential on \(\widehat{Y}\)
than \(X_1\). Using the partial-dependence-based variable importance
metric, we obtain \(i\left(X_1\right) = 1.4828203\) and
\(i\left(X_2\right) = 0.8961719\) which gives the ratio
\(i\left(X_2\right) / i\left(X_1\right) \approx 1.65\). In fact, we can
compute the ratio of the true variances of \(f_1\left(X_1\right)\) and
\(f_2\left(X_1\right)\):

\begin{equation*}
Var\left[f_2\left(X_2\right)\right] / Var\left[f_1\left(X_1\right)\right] = \left(5^2 / 12\right) / \left(3^2 / 12\right) = \left(5 / 3\right)^2.
\end{equation*}

Taking the square root gives \(5 / 3 \approx 1.67\).

Using the absolute value of the \(t\)-statistic becomes less useful in
linear models when, for example, a predictor appears in multiple terms
(e.g., interaction effects and polynomial terms). The partial dependence
approach, on the other hand, does not suffer from such drawbacks.

\subsection{Detecting interaction effects}
\label{sec:interaction}

As it turns out, our partial dependence-based variable importance
measure \eqref{eqn:vi} can also be used to quantify the strength of
potential interaction effects. Let \(\i\left(x_i, x_j\right)\)
\(\left(i \ne j\right)\) be the standard deviation of the joint partial
dependence values \(\bar{f}_{ij}\left(x_{ii'}, x_{jj'}\right)\) for
\(i' = 1, 2, \dots, k_i\) and \(j' = 1, 2, \dots, k_j\). Essentially, a
weak interaction effect of \(x_i\) and \(x_j\) on \(Y\) would suggest
that \(i\left(x_i, x_j\right)\) has little variation when either \(x_i\)
or \(x_j\) is held constant while the other varies.

Let \(\boldsymbol{z}_s = \left(x_i, x_j\right)\), \(i \neq j\), be any
two predictors in the feature space \(\boldsymbol{x}\). Construct the
partial dependence function \(\bar{f}_s\left(x_i, x_j\right)\) and
compute \(i\left(x_i\right)\) for each unique value of \(x_j\), denoted
\(\i\left(x_i | x_j\right)\), and take the standard deviation of the
resulting importance scores. The same can be done for \(x_j\) and the
results are averaged together. Large values (relative to each other)
would be indicative of possible interaction effects.

\section{Friedman's regression problem}

To further illustrate, we will use one of the regression problems
described in \citet{multivariate-friedman-1991} and
\citet{bagging-breiman-1996}. The feature space consists of ten
independent \(\mathcal{U}\left(0, 1\right)\) random variables; however,
only five out of these ten actually appear in the true model. The
response is related to the features according to the formula

\begin{equation*}
Y = 10 \sin\left(\pi x_1 x_2\right) + 20 \left(x_3 - 0.5\right) ^ 2 + 10 x_4 + 5 x_5 + \epsilon,
\end{equation*}

where \(\epsilon \sim \mathcal{N}\left(0, \sigma^2\right)\). Using the R
package \texttt{nnet} \citep{venables-modern-2002}, we fit a NN with one
hidden layer containing eight units and a weight decay of 0.01 (these
parameters were chosen using 5-fold cross-validation) to 500
observations simulated from the above model with \(\sigma = 1\). The
cross-validated \(R^2\) value was 0.94.

Variable importance plots are displayed in Figure \ref{fig:network-vip}.
Notice how the Garson and Olden algorithms incorrectly label some of the
features not in the true model as ``important''. For example, the Garson
algorithm incorrectly labels \(x_8\) (which is not included in the true
model) as more important than \(x_5\) (which is in the true model).
Similarly, Oden's method incorrectly labels \(x_{10}\) as being more
important than \(x_2\). Our method, on the other hand, clearly labels
all five of the predictors in the true model as the most important
features in the fitted NN.

\begin{figure}

{\centering \includegraphics[width=1.0\linewidth]{figures/network-vip-1}

}

\caption{Variable importance plots for the NN fit to the Friedman regression data. \textit{Left:} partial dependence-based method. \textit{Middle:} Garson's method. \textit{Right:} Olden's method.}\label{fig:network-vip}
\end{figure}

We also constructed the partial dependence functions for all pairwise
interactions and computed the interaction statistic discussed in Section
\ref{sec:interaction}. The top ten interaction statistics are displayed
in Figure \ref{fig:network-int}. There is a clear indication of an
interaction effect between features \(x_1\) and \(x_2\), the only
interaction effect present in the true model.

\begin{figure}

{\centering \includegraphics[width=1.0\linewidth]{figures/network-int-1}

}

\caption{Variable importance-based interaction statistics from the NN fit to the Friedman regression data set.}\label{fig:network-int}
\end{figure}

In fact, since we know the distributions of the predictors in the true
model, we can work out the true partial dependence functions. For
example, for the pairs \(\left(x_1, x_2\right)\) and
\(\left(x_1, x_4\right)\), we have

\begin{equation*}
f\left(x_1, x_2\right) = 10 \sin \left(\pi x_1 x_2\right) + 55 / 6,
\end{equation*}

and

\begin{equation*}
f\left(x_1, x_4\right) = \frac{5 \pi x_1 \left(12 x_4 + 5\right) - 12 \cos \left(\pi x_1\right) + 12}{6 \pi x_1}.
\end{equation*}

Next, we simulated the standard deviation of \(f\left(x_1, x_2\right)\)
for a wide range of fixed values of \(x_2\); this is what
\(i\left(x_1 | x_2\right)\) is trying to estimate. The results from
doing this for both predictors in each model are displayed in
Figure\textasciitilde{}\ref{fig:interaction-simulation}. The top row of
Figure\textasciitilde{}\ref{fig:interaction-simulation} illustrates that
the importance of \(x_1\) (i.e., the strength of its relationship to the
predicted outcome) heavily depends on the value of \(x_2\) and vice
versa (i.e., an interaction effect between \(x_1\) and \(x_2\)). In the
bottom row, on the other hand, we see that the importance of \(x_1\)
does not depend on the value of \(x_4\) and vice versa (i.e., no
interaction effect between \(x_1\) and \(x_4\)).

\begin{figure}

{\centering \includegraphics[width=1.0\linewidth]{figures/interaction-simulation-1}

}

\caption{Results from a small Monte Carlo simulation on the interaction effects between $x_1$ and $x_2$ (top row), and $x_1$ and $x_4$ (bottom row).}\label{fig:interaction-simulation}
\end{figure}

\subsection{Friedman's $H$-statistic}

An alternative measure for the strength of interaction effects is known
as Friedman's \(H\)-statistic \citep{friedman-2008-predictive}.
Coincidentally, this method is also based on the estimated partial
dependence functions of the corresponding predictors, but uses a
different approach.

For comparison, we fit a GBM to the Friedman regression data from the
previous section. The parameters were chosen using 5-fold
cross-validation. We used the R package \texttt{gbm} \citep{gbm-pkg}
which has built-in support for computing Friedman's \(H\)-statistic for
any combination of predictors. The results are displayed in Figure
\ref{fig:gbm-int}. To our surprise, the \(H\)-statistic did not seem to
catch the true interaction between \(x_1\) and \(x_2\). Instead, the
\(H\)-statistic ranked the pairs \(\left(x_8, x_9\right)\) and
\(\left(x_7, x_{10}\right)\) as having the strongest interaction
effects, even though these predictors do not appear in the true model.
Our variable importance-based interaction statistic, on the other hand,
clearly suggests the pair \(\left(x_1, x_2\right)\) as having the
strongest interaction effect.

\begin{figure}

{\centering \includegraphics[width=1.0\linewidth]{figures/gbm-int-1}

}

\caption{Interaction statistics for the GBM model fit to the Friedman regression data. \textit{Left:} Friedman's $H$-statistic. \textit{Right:} Our variable importance-based interaction statistic.}\label{fig:gbm-int}
\end{figure}

\section{Application to model stacking}
\label{sec:ensemble}

In the Ames housing example, we used a GBM to illustrate the idea of
using the partial dependence function to quantify the importance of each
predictor on the predicted outcome (in this case,
\texttt{Log\_Sale\_Price}). While the GBM model achieved a decent
cross-validated \(R^2\) of 91.54\%, better predictive performance can
often be attained by creating an \emph{ensemble} of learning algorithms.
While GBMs are themselves ensembles, we can use a method called
\emph{stacking} to combine the GBM with other cutting edge learning
algorithms to form a \emph{super learner} \citep{stacked-wolpert-1992}.
Such stacked ensembles tend to outperform any of the individual base
learners (e.g., a single RF or GBM) and have been shown to represent an
asymptotically optimal system for learning \citep{super-laan-2003}.

For the Ames data set, in addition to the GBM, we trained and tuned a RF
using 10-fold cross-validation. The cross-validated predicted values
from each of these models were combined to form a \(n \times 2\) matrix,
where \(n = 1460\) is the number of training records. This matrix,
together with the observed values of \texttt{Log\_Sale\_Price}, formed
the ``level-one'' data. Next, we trained a \emph{metalearning}
algorithm---in this case a GLM---on the level-one data to create a
stacked ensemble. To generate new predictions, we first generate
predictions from the RF and GBM learners, then feed those into the GLM
metalearner to generate the ensemble prediction.

Even though the base learners in this example have the built-in
capability to compute variable importance scores, there is no way of
constructing them from the super learner. However, since we can generate
predictions from the super learner, we can easily construct partial
dependence functions for each predictor and use Equation \eqref{eqn:vi}.
The left and middle plots in Figure \ref{fig:ames-ensemble-vip} display
the top 15 variable importance scores from the individual base learners.
The right plot displays the top 15 variable importance scores
constructed using Equation \eqref{eqn:vi} for the combined super
learner. All models seem to agree on the top three predictors of
\texttt{Log\_Sale\_Price}; namely, \texttt{Overall\_Qual},
\texttt{Neighborhood}, and \texttt{Gr\_Liv\_Area}.

\begin{figure}

{\centering \includegraphics[width=1.0\linewidth]{figures/ames-ensemble-vip-1}

}

\caption{Variable importance plots for the Ames data set. \textit{Left:} RF. \textit{Middle:} GBM. \textit{Right:} Stacked ensemble (i.e., super learner).}\label{fig:ames-ensemble-vip}
\end{figure}

\section{Application to automatic machine learning}
\label{sec:automl}

The data science field has seen an explosion in interest over the last
decade. However, the supply of data scientists and machine learning
experts has not caught up with the demand. Consequently, many
organizations are turning to automated machine learning (AutoML)
approaches to predictive modelling. AutoML has been a topic of
increasing interest over the last couple of years and open source
implementations---like those in H2O and auto-sklearn
\citep{auto-sklearn}---have made it a simple and viable options for real
supervised learning problems.

Current AutoML algorithms leverage recent advantages in Bayesian
optimization, meta-learning, and ensemble construction (i.e., model
stacking). The benefit, as compared to the stacked ensemble formed in
Section \ref{sec:ensemble}, is that AutoML frees the analyst from having
to do algorithm selection (e.g., ``Do I fit an RF or GBM to my data?'')
and hyperparameter tuning (e.g., how many hidden layers to use in a
DNN). While AutoML has great practical potential, it does not
automatically provide any useful interpretations---AutoML is
\textbf{not} automated data science \citep{mayo_2017}. For instance,
which variables are the most important in making accurate predictions?
How do these variables functionally related to the outcome of interest?
Fortunately, our approach can still be used to answer these two
questions simultaneously.

To illustrate, we used the R implementation of H2O's AutoML algorithm to
model the airfoil self-noise data set \citep{uciml} which are available
from the University of California Machine Learning Repository:
\url{https://archive.ics.uci.edu/ml/data sets/airfoil+self-noise}. These
data are from a NASA experiment investigating different size NACA 0012
airfoils at various wind tunnel speeds and angles of attack. The
objective is to accurately predict the scaled sound pressure level (dB)
(\texttt{scaled\_sound\_pressure\_level}) using frequency (Hz)
(\texttt{frequency}), angle of attack (degrees)
(\texttt{angle\_of\_attack}), chord length (m) (\texttt{chord\_length}),
free-stream velocity (m/s) (\texttt{free\_stream\_velocity}), and
suction side displacement thickness (m)
(\texttt{suction\_side\_displacement\_thickness}). H2O's current AutoML
implementation trains and cross-validates an RF, an extremely-randomized
forest (XRF) \citep{extremely-geurts-2006}, a random grid of GBMs, a
random grid of DNNs, and then trains a stacked ensemble using the
approach outlined in Section \ref{sec:ensemble}. We used 10-fold
cross-validation and RMSE for the validation metric. Due to hardware
constraints, we used a max run time of 15 minutes (the default is one
hour). The final model consisted of one DNN, a random grid of 37 GBMs,
one GLM, one RF, one XRT, and two stacked ensembles. The final stacked
ensemble achieved a 10-fold cross-validated RMSE and R-squared of 1.43
and 95.68, respectively.

Since predictions can be obtained from the automated stacked ensemble,
we can easily apply Algorithm \ref{alg:pdp} and construct PDPs for all
the features; these are displayed in Figure \ref{fig:airfoil-aml-pdps}.
It seems frequency (Hz) and suction side displacement thickness (m) have
a strong monotonically decreasing relationship with scaled sound
pressure level (dB). They also appear to be more influential than the
other three. Using Equation \eqref{eqn:vi}, we computed the variable
importance of each of the five predictors which are displayed in Table
\ref{tab:airfoil-aml-vi}.

\begin{figure}

{\centering \includegraphics[width=1.0\linewidth]{figures/airfoil-aml-pdps-1}

}

\caption{PDPs for the five features in the airfoil self-noise data set based on an AutoML algorithm consisting of DNNs, GBMs, GLMs, RFs, XRTs, and stacked ensembles. The dashed red line in each plot represents the mean scaled sound pressure level (dB) in the training data.}\label{fig:airfoil-aml-pdps}
\end{figure}

\begin{table}[!htb]
  \centering
  \begin{tabular}{lr}
    \toprule
    Variable                                & Importance \\
    \midrule
    Angle of attack (degrees)               & 0.4888482  \\
    Free stream velocity (m/s)              & 1.1158569  \\
    Chord length (m)                        & 1.4168613  \\
    Suction side displacement thickness (m) & 3.3389105  \\
    Frequency (Hz)                          & 5.4821362  \\
    \bottomrule
  \end{tabular}
  \caption{Partial dependence-based variable importance scores for the five predictors in the airfoil self-noise data set based on an AutoML algorithm consisting of DNNs, GBMs, GLMs, RFs, XRTs, and stacked ensembles. \label{tab:airfoil-aml-vi}}
\end{table}

\section{Discussion}
\label{sec:conc}

We have discussed a new variable importance measure that is (i) suitable
for use with any supervised learning algorithm, provided new predictions
can be obtained, (ii) model-based and takes into account the effect of
all the features in the model, (iii) consistent and has the same
interpretation regardless of the learning algorithm employed, and (iv)
has the potential to help identify possible interaction effects. Since
our algorithm is model-based it requires that the model be properly
trained and tuned to achieve optimum performance. While this new
approach appears to have high utility, more research is needed to
determine where its deficiencies may lie. For example, outliers in the
feature space can cause abnormally large fluctuations in the partial
dependence values \(\bar{f}\left(x_{i}\right)\)
\(\left(i = 1, 2, \dots, k\right)\). Therefore, it may be advantageous
to use more robust measures of spread to describe the variability in the
estimated partial dependence values; a reasonable choice would be the
median absolute deviation which has a finite sample breakdown point of
\(\left\lfloor{k / 2}\right\rfloor / k\). It is also possible to replace
the mean in step (3) of Algorithm \ref{alg:pdp} with a more robust
estimate such as the median or trimmed mean. Another drawback is the
computational burden imposed by Algorithm \ref{alg:pdp} on large data
sets, but this can be mitigated using the methods discussed in
\citet{pdp-greenwell-2017}.

All the examples in this article were produced using R version 3.4.0
\citep{R}; a software environment for statistical computing. With the
exception of Figure \ref{fig:interaction-simulation}, all graphics were
produced using the R package \texttt{ggplot2} \citep{pkg-ggplot2};
Figure \ref{fig:interaction-simulation} was produced using the R package
\texttt{lattice} \citep{pkg-lattice}.

\bigskip

\begin{center}
{\large\bf SUPPLEMENTARY MATERIAL}
\end{center}

\begin{description}

\item[R package:] The R package `vip`, hosted on GitHub at \url{https://github.com/AFIT-R/vip}, contains functions for computing variable importance scores and constructing variable importance plots for various types of fitted models in R using the partial dependence-based approach discussed in this paper.

\item[R code:] The R script `vip-2018.R` contains the R code to reproduce all of the results and figures in this paper.

\end{description}

\bibliographystyle{agsm}

redpajama_set_name: RedPajamaArXiv


If I could describe Florence and the Machine's latest album, High as Hope, in one word, it would be goosebumps. I was merely 45 seconds into the first song and I had goosebumps up and down my arms. The last time I felt this way about a record, let alone a single song, was Harry Styles' debut album that was released last May; more specifically, his first single, "Sign of the Times". I would do anything to go back to that first time hearing his voice sing a stunning, five and a half minute song that truly separated him from his boyband days. Although, the thing with Florence is that she is not doing anything out of her comfort zone. She is not shocking anybody. She simply delivers time and time again. And I wish the same thing when it comes to each of her songs: that I could erase it from my memory and hear it all from the beginning without knowing how spectacular her voice is going to be.
Love is an underlying theme on this record, but not necessarily bubblegum pop love between a boy and a girl. The love sung about on High as Hope focuses on coming together to find love in a situation, find light, find a community; growing together, flourishing together. As well as self-love, where one comes to terms with who they are and works for themselves, because loving yourself first is vital. As written in The Perks of Being a Wallflower, "We accept the love we think we deserve." I believe that Florence feels that now, as she herself has lived a life of love and loss, both internally and externally. There was a time where fame and her didn't mix very well and found herself on a path of binge-drinking, anguish, and sorrow. She recounts this confusing and conflicting time in her life in an soul crushing, yet equally sweet and mesmerizing song entitled "South London Forever".
"South London Forever" is actually a quite up-tempo, summery track that brings the true emotions behind the song to the table through its lyrics. "And I go home alone/I drive past the place that I was born/And the places that I used to drink/Young and drunk and stumbling in the street," the song begins with harsh truth that Florence knows all too well. She continues on, later crooning and questioning, "But did I dream too big? Do I have to let it go?" Her rambunctious, dangerous, reckless youth is on full display, but her inner feelings are all she wanted to showcase. Her voice flutters alongside the evenly paced drum beats and twinkling piano sounds, where all she sings explain her cries for attention and her inner turmoil that she can speak about now, as she has grown. She can remember South London and all that came with it, but she has moved forward and can sing about it now, albeit with eye opening emotions running high. The production on High as Hope marks a turning point in her career. There are many tracks that are stripped down, keeping the synth and keyboard's purpose subtle, almost effortless; although necessary. The stylistic choices made are what create the album and made it as divine as it is. "Patricia", an ode to Patti Smith, is one of the least personal tracks found on this new album, but not at all closed up. Patti Smith is an inspiration of Florence's, so that in itself is personal, but it proves that a song can still be energetic and wholesome, even if stripped of elaborate, often over-the-top production and extensive, occasionally unnecessary instrumentals. Nothing on this new Florence and the Machine album is unnecessary, as each and every bang of the drum and strum of the guitar was particularly curated and chosen to build the story of the song, not mask it or overpower it. Welch found a home in this record; a home that she is comfortable in, one that is filled with acceptance, love, and growth. Baring her soul within her songs is not a new feat for her, but the approach and style of which she is doing it has stunned us all. At only 31 years old, she is able to look back, feel nostalgic, and show us all that it is never too early or too late to find your inner strength or work toward self-love. She did it. She sings about it, in a beautiful voice that speaks to each listener's soul. How? Because her words come from her soul, her mind, and her heart. That is what gives me goosebumps.


text:
Cedar Lake Speedway Announces Class Sponsors for 2021 Season
By CLS, 04/01/21, 10:30PM CDT
Cedar Lake Speedway is thrilled to announce sponsorships for each of the following classes during the 2021 season:
Pro-Stock: Matco Tools by John Olson
Midwest Modified: Viebrock Performance Products
Limited Late Model: The Moes Sell Homes
Traditional Sprint Car: GRP Motorsports
A-Modified: Kelly Iverson Trucking
Late Model: Sabre Plumbing, Heating, & Air Conditioning
"We are excited to announce these sponsors before our racing begins in April", stated Chuck Kaufman. "Each sponsor has a long standing relationship with racing and Cedar Lake Speedway and is proud to associate their company name with their respective class. These partnerships solidify a strong commitment to our drivers as well contributing to the overall positive growth for each class".
Matco Tools by John Olson is a professional distributor/retailer of over 13,000 high quality tools primarily for the automotive industry. Matco Tools by John Olson serves St. Paul and Woodbury, Minnesota areas and can be contacted at (612) 850-7354.
Veibrock Performance Products is a race car part retailer of numerous name brand parts and is the official race car part vendor at Cedar Lake Speedway. Viebrock Performance Products, owned & operated by Kevin Viebrock, is located in Somerset, Wisconsin and can be contacted at (715) 247-3666.
The Moes Sell Homes is a full service real estate agency serving the needs of residential and commercial buyers and sellers in the western Wisconsin area. Jim Moe and family are strong supporters of racing at Cedar Lake Speedway for 20+ years and can be contacted at (715) 781-0387.
GRP Motorsports is owned and operated by Greg Parent and is based in Stillwater, Minnesota. Greg Parent is a long standing supporter of Cedar Lake Speedway, UMSS, and numerous racers across a wide range of race classes (novice to expert level). Greg Parent can be contacted at (651) 430-9861.
Kelly Iverson Trucking is a licensed and bonded freight shipping and trucking company located in Eleva, Wisconsin. Kelly Iverson Trucking provides LTL freight delivery of general freight such as household goods, chemicals, paper products, and construction materials and can be contacted at (715) 287-4579.
Sabre Plumbing, Heating, and Air Conditioning is an indoor comfort specialist providing quality and affordable heating, air conditioning and plumbing services. Sabre Plumbing, Heating, and Air Conditioning is located in Plymouth, Minnesota and can be contacted at (763) 473-2267.
Tag(s): Press Releases
This website is powered by SportsEngine's Sports Relationship Management (SRM) software, but is owned by and subject to the Cedar Lake Speedway and Arena privacy policy. ©2021 SportsEngine, Inc.

redpajama_set_name: RedPajamaCommonCrawl


When WPX changed the multi-single rules to allow a second multiplier-only rig, a new requirement was added that the multiplier transmitter was to use its own set of serial numbers. When two NA computers are linked together for WPX m/s, this is not possible.
"I will take whatever you can generate that will give me a log with the correct serial numbers sent by each station. If it is consecutive serial numbers that is fine. If it is separate numbers on each band as in M/M, that will be fine also. My original intent 2 years ago was just to have one set of consecutive serial numbers. For some software (not yours), that proved to be a major problem. The work-around was to set up as a multi-multi which worked okay until they merged the log and the software renumbered the log. I've not had any experience using NA linked so I don't have a good idea how you handle serial numbers."
"The intent is not to make it complicated, just to make sure I get a log with the correct serial numbers. No one will be penalized if they use consecutive numbers or numbers by band or by transmitter."
My recommendation would be to continue to set up WPX multi-singles without using "QSO Number By Band", and as Steve has indicated above, will meet their logchecking needs as well.


In recent years, the degree of automation of industrial equipment is getting higher and higher, so the number of industrial control boards in various industries is also increasing. After the industrial control boards are damaged, the high cost of replacing the Circuit Board (a few thousand yuan, more tens of thousands or one hundred thousand) has also become a headache for businesses. Circuit Board maintenance as a new repair industry, also in rapid development. Damaged circuit boards are mostly repairable in China and save 70-80% of the cost of buying a new board, and take much less time than setting boards abroad. Here are the basics of circuit board maintenance.
Almost all PCB circuit board repairs do not have drawing materials, so many people are skeptical about circuit board repairs, although the various circuit boards vary widely But what remains constant is that each type of circuit board is made up of a variety of integrated blocks, resistors, capacitors, and other components, so the damage to the circuit board must be caused by damage to one or more of these components The idea of circuit board maintenance is based on the above factors. Circuit Board maintenance is divided into detection and maintenance of two parts, which occupies a very important position. Every component on the circuit board to repair the basic knowledge of the test, until the bad find replacement, then a circuit board is repaired.
Board detection is the process of finding, identifying and correcting the faults of every electronic component on a board. In fact, the whole test process is the thinking process and the test process that provides the logical reasoning clue. Therefore, the test engineer must accumulate experience and improve his level gradually in the process of circuit board maintenance, test and repair.
General electronic equipment is composed of thousands of components, in the maintenance, overhaul, rely on direct test PCB board in each component to find the problem will be very time-consuming It's also very difficult to implement. So from the fault phenomenon to the cause of the fault, the type of maintenance, is an important method of maintenance. Once the circuit board has been detected, it is easy to fix.


text:
Constant Contact Add On
If you've been thinking about trying out Constant Contact, you've come to the right place. Constant Contact is an online marketing company with offices in Waltham, Massachusetts, Loveland, Colorado, and New York, and a website that lets you create, send, and manage email lists. With this in-depth review, you'll be better equipped to decide whether it's the right fit for you. Let's get started!
Constant Contact has a few drawbacks. It lacks automation features like branching points or splits. Constant Contact doesn't offer personalized email, as some email providers do. Workflows start with a trigger. This could be an email or a site visit. Depending on your needs, you can change the order of emails or add delays between them. Constant Contact offers integrations with third party applications like Vimeo. This integration allows you to create automated email workflows for events and sell digital products. You can also respond to abandoned shopping carts.
Another benefit of Constant Contact is that it lets you create attractive signup forms for each visitor to your website. It is easy to create signup forms using its intuitive interface. You can copy it and paste it into your website. Constant Contact's signup forms can be tailored to the interests of your website visitors. Then, you're ready to launch your email campaign! Once you've established your list, you can begin sending targeted emails.
Another great feature of Constant Contact is its ability to segment its lists by tags. You can send targeted email to people who match certain tags or interests in your business. Constant Contact offers detailed reporting as well. The best part? It's free to use. You can sign up for a free 60-day trial to see it in action. Just make sure you check the free trial period and read about all the features and pricing.
Constant Contact's email marketing tools are affordable and easy to use. Even newbie email marketers can understand the platform's interface. The software promises high deliverability, which means less chances of emails getting caught in spam filters. It also offers niche-specific features such as Eventbrite and Google Analytics integrations. Constant Contact can also be used to integrate social media accounts such as Facebook. You can also create Facebook ads or integrate campaigns.
Another great feature of Constant Contact is its email editor. It lets you send automated emails to new subscribers. You can also track the performance of your email. In addition to emailing subscribers, you can also segment your contacts to send them specific messages and special offers. The Constant Contact email editor allows you to segment contacts. If you have a Shopify-based store, Constant Contact can help you amplify your efforts. The program syncs your contacts and products automatically, allowing you to send targeted emails and offers to customers. You can recover lost sales and customers with Constant Contact.
Constant Contact also offers a drag-and-drop editor. You can add design elements and layouts to emails. It also allows you to preview your emails in many popular email clients. With Constant Contact, you won't have to learn coding to create stunning emails. Constant Contact is the best choice for beginners in email design. This email editor can be used for a variety of purposes and is perfect for beginners.
Constant Contact also offers intuitive marketing tools that can be used by small businesses and non-profits to build their email lists. They offer email marketing, social campaign management, and event marketing. Other useful features include reporting and survey management, and drag-and-drop email templates. Constant Contact can manage all your email design and communication requirements. Because Constant Contact offers customizable templates, it is also a great option for e-commerce merchants. However, advanced creative designers will find Constant Contact a little lacking in customization.
Pricing depends on the size of your email list and how many you wish to send. There are two plans available: email Plus. The latter includes a range of additional features, including coupons, surveys, and registration forms. Constant Contact is priced fairly competitively in the market, but it falls behind Editors' Choice winner Campaigner. You can also find a free trial plan to try out Constant Contact and get started for less.
The Constant Contact Reporting tab provides a summary of the statistics for your email campaigns. To compare each email's performance with its competitors, you can filter the data by campaign name or time frame. You can also export this data to Excel and CSV. Constant Contact also has a mobile app. Those with large contact lists might want to opt-in to a monthly plan. If you don't require advanced features, this can be expensive.
How To Send Out An Email In Cyrillic From Constant Contact
Create A Constant Contact Template With Indesign


Both Tales of Graces (4.5 stars) and Tales of Vesperia (4 stars) have even scored higher on Amazon than Final Fantasy XIII.
We should compare the U.S. Amazon site for the game reviews and see how they did. I'm not sure how the wolf shirt fits into the equation even though the reviews were hilarious.
Did you read any of the reviews on the Amazon link I posted a few days ago?
They might be a bit more politely written than a typical mediocre-to-bad English Amazon review, but I wouldn't call them objective at all. Or even particularly descriptive. At least the random sampling that I glanced through. This is dame, that is dame, everything is super dame. And comments like "It's not a game, it's a movie!" are extraordinarily hyperbolous. It's not that people can't make comments like that, but every comment in that vein takes level of reasonability down a notch.
Anyways, Amazon isn't a great measuring stick for overall opinion. And for the first few months or so, reviews and impressions aren't either. To get a genuine outlook on how the Japanese (and later, North Ameria and Europe) will have received it, we need to wait. It's the same way with most games--that simple. It's like water sloshing back and forth between two extremes. We need to let things calm down before we can see what kind of long-term impact the game will have. Any attempts to declare it now would be heavy handed.
You're absolutely right about people's expectation of what the game might be like, and how they react when their expectations are shaken. If people can get around their notions, I think they'll enjoy the game a lot more. For some, RPG tropes might be a stumbling block they can't get over, unfortunately.
On the characters, I agree to a point. I think all the pieces were there for the story and characters. It could have been awesome. It could have been really awesome. The personalities that showed up during the game were well-packaged and consistent. I found Sazh and Vanille (perhaps despite myself) extremely likeable.
I feel that the game didn't put all the pieces together correctly. The Japanese dialogue was not overall great, and while some of the individual scenes had what seemed like real emotion and strength behind them, the lack of context behind them greatly lessened their emotional impact. Some of them felt out-of-the-blue in a bad way, like Lightning realizing that she has (apparently) been following fal'Cie blindly all her life (the "fal'Cie leash" comments that made it into one of the English trailers). Why? How? In what way? We don't get a real working glimpse of how her world works to understand those feelings, so the scene felt contrived to me. Lots of scenes did. Vanille, Hope and Fang's pre-summon-battle scenes felt like this too. They all went from calm to extremely distressed in a very short period of time, but I couldn't identify with them, unlike Lightning, Snow and Sazh.
The lack of towns (in the traditional sense) doesn't significantly hurt the gameplay. I think it hurts the story more. It means that the player doesn't get a very good natural worldview, and you lose a lot of the subtle contexts and characterizations that come with towns and NPCs. The few times we got NPC interaction (opening chapters and Nautilus, and maybe a bit in Palumpolum), some of it was frivolous, yes, about plushies and junk, but some of it boosted my grasp of Cocoon just a bit. Say what you will about Final Fantasy XII, but that was a game whose world was significantly enhanced by the NPCs (whose comments changed after nearly everything). What the story didn't hand on a platter to you, many of the gaps were filled in by the people of Rabanastre, in a way. But Final Fantasy XIII doesn't fill those gaps.
The opening cinema to Chapter 12 was the epitome of the large, self-indulgent cinematics that the game favours over small stuff that might have done it better. When I watched it, I felt both drawn in and jarred out of the experience. I think I shook my head and laughed, too, as soon as the party hit the racetrack and all that ensued afterwards. It's very flashy, very shaky cam, very action movie. It was thrilling, but shallow.
The game experience was amazing in many ways, and very deliberately crafted, but I wish they had polished the story up.
Having played the game myself, seeing the Amazon reviews and numerous magazine reviews in both English and Japanese, and even reading what posting with others on this thread and other various places, I think the above is a pretty fair assessment of the game in short.
I also agree that the story falls far short of greatness. I really wanted to like the characters because they are well designed but if anyone tried to pass the story for this game off as a book, we would see a worldwide book burning because of the shallowness of nearly everything. Final Fantasy XIII is the game that tries to do so much and then misses the point on almost everything. Final Fantasy XIII is comparable to Metal Gear Solid 4 in being much more of a movie than a game.
Uncharted 2: Among Thieves on the other hand does almost everything RIGHT that Final Fantasy XIII does WRONG. The best part about Uncharted 2 is that you actually get to PLAY the game in the cool scenes while in Final Fantasy XIII, you'd better pull up a banana chair, get comfy, and be prepared to be UNimpressed by the dialogue in almost every scene while getting an eyeful of the flashiest pre-rendered movies we've seen yet on a console.
Wow. After four straight pages of posts, there has been only ONE story spoiler in this entire thread.
Well put, Tip of Tail.
Fine, if that's how you're going to be.
There, I've spoiled the ending. Happy now?
Hah, I'm actually not going to read that - despite trying to mod this forum, I'm doing my best to keep spoiler free until I play the game. I'm just amused that despite this thread existing for spoilers because people supposedly wanted to discuss the spoiler portions of the game, this has focused entirely on Amazon review scores.
Rank system - from what I know, it's entirely based on time taken which is calculated from your parties stats.
What about battles change when you consistently get 5 star rank on battles?
Is there any uber post-game boss that makes players cry hot tears of shame? Because all the post game vids I've seen in videos so far is overpowered parties blowing up anything in their path. Apparently some kind of 'optima-cancel' has been discovered that makes the game a complete joke.
I saw a really bad thing with XII, and this confirms my doubts about XIII.
I Don't understand how they can let the creator of FF go away, and expect to make the games that we all love.
There is a reason we are all buying Final Fantasy after Final Fantasy - and that reason is somewhere in the head of Hironobu Sakaguchi.
You can't just try to mimic that kind of talent.
Agreed. We haven't seen a really fleshed out Final Fantasy game since he left. The stories we're getting now are empty and dull. Hironobu Sakaguchi has often been referred to as a control freak when it comes to his games but we tend to get something good when he's in charge because he doesn't settle for anything less than what he believes his team is capable of.
In a nutshell, he's like the Steve Jobs of gaming. And Square-Enix, well...they're kind of like Microsoft who blesses us with their mediocre crap 90% of the time.
I was willing to buy that Hironobu Sakaguchi was the driving force behind the series until played Lost Odyssey.
I'll agree that it wasn't the perfect game (some strange animations, toned down battle system, etc.) but considering Mistwalker's limited budget, we got more bang for our buck than any Final Fantasy game has provided us since the last one he worked on which was Final Fantasy X. Lost Odyssey's story and characters are seriously so well developed in comparison to anything we got in Final Fantasy XI, XII, or XIII.
I'd like to ask that if we're going to talk about things not related to spoilers in the storyline of Final Fantasy XIII, please don't use this thread for it. There's a thread for general, spoiler-free discussion.
Haha lucky me, I thought I was in the spoiler free thread for a few minutes trying to track down the conversation that was going on in that one.
I shall now quickly depart this thread!
Says a person using spoiler tags in a spoiler thread.
-Game is taking a while to pick up..
..-Meaning not enough options in combat yet.
I was kind of surprised in the first dungeon. I thought the place was pretty large. It wasn't as massive as, say, Mass Effect, but not as empty either. Battles are fun so far, but there aren't enough moves, making them somewhat monotonous so far. I confess small agitation at not being able to choose my party members.
My jaw literally dropped at the opening FMV, it was spectacular, but standard cut scenes are somewhat less impressive, with some odd rough edges and some slightly unimpressive animations. Still, very detailed for the most part, and very good.
I like the voice acting, somewhat surprisingly, but the story so far hasn't done it for me. It feels to slow, but, it is a sixty hour game.
The game's battles are incredibly fast, and can get pretty intense...Normal encounters are usually only moderately challenging, but bigger enemies and bosses can really open a can if you're not careful. The first real boss killed me like three times, each time taking about nine seconds. Ultimately beating the boss took a little over a minute, though it certainly seemed longer, due to the aforementioned intensity.
I'm about 8 hours into the game, and I just got asked to put in Disk 2. This is really weird if you ask me, because I still can't even choose my party members.
The first disk of the game is kinda like...you run around like crazy, and you fight. In between you get a little back story. I hate that after 8 hours, I am still basically in the 'beginning' of the game. This is like the first half hour of most games...can't choose your party members, tons of random tutorials, don't really know where you are going or really why, who are the bad guys...this is RPG 101.
I just don't get it. I know that eventually when you get to pulse, you get some open ended game play, but I've heard even that isn't that balanced (stupid kill x monsters y times, running crazy around different plains, bad level design). Then it is back to single linear game play until the end.
Someone said it best on here, if you order cake and you get pizza, sure you are happy with pizza cause its good, but you wanted cake...it's semantics really. If they wanted to innovate this much with a game, then call it something else. I look forward to FF games because I know what kind of game play and story to expect.
I've been lurking around here for a long time, and this new FF made want to register to discuss a bit about the story.
Finished it a few days ago. I've mostly enjoyed the ride (even if there's not much replay value) but some part of the plot are still nebulous for me.
Mostly everything about Gran Pulse, so far what i understood there was a war a 100 years ago with Ragnarok cracking the shell of Cocoon but not finishing the job because of some goddess or traitor?(it's in datalog with the poems i think).
Since then Cocoon are scared of the Pulse menace, seems logical. But when your sent to Pulse there isn't a single human in sight. Also the only place with some trace of old life seems to be Oerba Vanille and Fang town? We don't even know what became of them. I'm guessing they all became Cie'th or Undying but it's a guess, the story doesn't explain it clearly if at all.
That's some random tought i had after the end of the game. It left me perplexed although i enjoyed the end since it left me with the usual strange "sadness-nostalgia?" feeling i have each time i finish a rpg i liked.
Yeah. This game takes some getting used to, but if you can learn to adapt and appreciate it for what it is, chances are you'll enjoy it on the whole. Not sayin' it's perfect, but it's not too shabby either. Of course, I can see how some folks would be put off by certain aspects. And I can also see those same people gnawing on their controllers in fits of blind frustration after getting their faces stomped by everything from PSICOM grunts to vicious feral creatures over and over again. But seriously, sometimes you just need to adapt, ya know? If you can't (or won't), then there's just no way you'll be any good at this game, which will naturally have an adverse affect on how much fun you end up having. Frustration and anger tend to mess with the fun factor.
Ah well, can't really expect to please everyone. I mean, I lit my copy of FFXII on fire on general principles, so I'm not really one to talk when it comes to game rage or whatever the hell people call it.
Second off as to the soundtrack, didn't like it, though, some of the songs were decent. I got to admit this is the first FF game in a while where I regretted buying the CD for it.
Third off, as to the level up system, they made grinding EVEN MORE TEDIOUS!! Towards the end it takes forever just to get one of your stats up.
Forth off, I don't like how they put some of the side quests AFTER you beat the game. The whole point IMO for the side quests were to make the leveling up for the end boss or any other boss much more easier and funner. Plus side quests should add to the story like in FF 12 (which now I know is one of my favorite of the FF series, especially after playing this train wreck of a game). Side quests shouldn't be generic like "oh this guy from this village needed to kill this enemy" and have such a generic format sound. If that's going to be the case, they should have not bothered even putting that little bit of information. They should have just said just go here and kill this.
Faustus: \"History is written by the winners, Kain!\"
I disagree that high expectations ruin this game.
As a dungeon crawler, I'd say the design is quite good. The problem is that most people weren't expecting a dungeon crawler.
I even see problems with it as a dungeon crawler because of all the long tunnels with little to branch out or explore. There's not even any good puzzles. If they wanted a pure fighter, they needed shorter dungeons and more fights. If they wanted a crawler, the dungeons needed to get more varied and interesting.
I like the *idea*, I just don't always like the execution.
I think of it as a more streamlined dungeon crawler. Many people don't like to play crawlers because they're tedious, long, and sometimes lack things like common save points. You could make some of these arguments for FFXIII, but at the same time it made the sub-genre a good deal more accessible to those who wouldn't typically play it. I find FFXIII a much easier game to get into and play for a short time than, say, SMT:SJ or The Dark Spire (though I do enjoy both - particularly the latter). I understand what you're saying, though.
That said, I think if more dungeons were like say, Taijin's Tower, the overall experience would be much better.
But when your sent to Pulse there isn't a single human in sight. Also the only place with some trace of old life seems to be Oerba Vanille and Fang town? We don't even know what became of them. I'm guessing they all became Cie'th or Undying but it's a guess, the story doesn't explain it clearly if at all.


London: Asia-focused bank Standard Chartered Plc. said on Tuesday that it is buying American Express Bank Ltd (Amex), a unit of American Express Co., for about $860 million (Rs3,492 crore) to help "turbo charge" the growth of its recently launched private banking business.
Standard Chartered said the purchase from Amex would accelerate the build-up of its private banking business by about three years and bring in Amex's Financial Institutions Group (FIG), which provides clearing, payments and trade services to other banks.
The transaction is expected to close by first quarter of 2008. Amex is present in 47 countries, and serves other financial institutions and high-net-worth individuals through its global correspondent banking business and a full-service private bank.
The deal should create pre-tax cost savings of more than $100 million per year from 2009 onwards, which is likely to include some job cuts, the UK-headquartered bank said. The purchase should be accretive to earnings in 2009—the first full year of ownership, after factoring in costs associated with integrating the businesses next year.
"I wouldn't expect it (the combined private banking business) will make a very significant profit contribution in 2008, the full potential will be coming through in 2009 and 2010 ... it's the ability to gain critical mass in a business that we see as a key strategic priority for our future," said Standard Chartered chief executive Peter Sands. The deal will be funded from existing resources and the bank's ongoing debt financing programme.
The Indian arm of Standard Chartered also stands to gain in its private banking operations. "Amex has a strong private banking client base in India," said Bala Swaminathan, managing director and regional head of client relationships, India and South Asia, Standard Chartered. The other benefit for the acquirer will be Amex's clearing business, which is a system of daily offsetting of debts between banks. So far, the Indian arm of Standard Chartered did not have direct access to the dollar clearing business. In addition, Amex will provide Standard Chartered with a direct Euro and Yen clearing capability. Amex "handles clearing for a lot of public sector banks in India, and we will now get access to that business as well," Swaminathan said.
Standard Chartered is the largest foreign bank in India in terms of network with 81 branches, against seven branches of Amex.
Mint's Gargi Banerjee contributed to this story.


Feel free to browse some of our sermons.
As part of our songs series, a church member shares his/her experience of the power of song.
During the month of July, we are privileged to hear reflections on Wisdom and Wonder. We are grateful for the voices and experiences of our church members.
Over the next several weeks, you'll be hearing from church members about why they give, and why this church is important. Why do you give? Why is this church important to you? Read more about Our Path to Commitment.
This Ash Wednesday meditation is a reflection on Jan Richardson's poem Return.
Sunday's sermon reflected on the recent General Assembly. Several blog posts highlight the assembly.


text:
For A Rightful Place: UNSC Reforms and Japan
Prof. Mohammed Badrul Alam
As per the UN High-Level Panel report on Threats, Challenges, and Changes, [titled 'A more secure world: our shared responsibility'], two options were recommended for broadening the current representation of the UN Security Council. This was done primarily with the objective of providing geographical balance and changing power equations since the end of World War II and the creation of the United Nations.
Under Model-A, six permanent members would be added to the Security Council, two each from Asia and Africa, one each from Europe and the Americas. Also, three new non-permanent members are to be added to the existing quota of ten members. In broad terms, countries of G-4 (Japan, India, Germany, Brazil) are in favor of this arrangement whereas the 'coffee club' of 40 mid-size countries led by Argentina, Pakistan, Italy, Mexico, South Korea and Spain are against Model A. China, in recent days, have voiced its opposition to Japan's entry into this exclusive club accusing the latter of being arrogant and insincere in offering apology over Japan's conduct during World War II. The United States, on the other hand, has voiced support for the inclusion of G-4, although it is ambivalent on the veto granting issue.
Model- B provides induction of eight 'semi-permanent' members with a renewable term of four years and one new non-permanent member. Predictably, the G-4 nations are against Model B. Conversely, the countries opposed to Model A are backing Model B. For most countries of Africa and Latin America, Model B provides them an assured stint of a much longer duration at the UNSC.
During the April 2005 visit of Japanese Premier Junichiro Koizumi to India, Japan made a tactical understanding with India in pushing the diplomatic envelope with veto as its ace card. However, an analysis of veto use by P-5 would show that veto is not so much of a positive power as having a negative connotation. Article 27 of the U.N. Charter, which specifies the voting procedure for adopting resolution make provisions on procedural matters to be made by an affirmative vote of nine out of fifteen. Only on non-procedural matters, the affirmative vote of nine must include the concurring votes of the five permanent members. During the Cold war, the United States had used its veto rights most often particularly when it affected its closet ally in the Middle East, Israel.
Under this backdrop it would be of interest to analyze the rationale behind Japan's interests in pushing for the permanent membership of the expanded Security Council.
First, Japan contributes roughly 20% of total UN budget thus surpassing 15% of contribution provided to UN body by P-4 countries combined - excluding the US. Japan has also made tangible contribution to UN Peace Keeping Operations (PKO) that is 20% and in actual figures is around 4.5 billion US dollars. Apart from financial contribution, Japan dispatched 1,220 military personnel to Cambodia; 2,300 military staff to East Timor and over 1,000 S.D.F units to Iraq and Afghanistan in the recent past.
Second, when the UN was created in 1945, there were only 51 member states, whereas that number has now quadrupled to 191 states. In order for proper governance and democratic legitimacy in a transparent way, Japan feels there should be a fair level of representational balance. In 1945, one member in the Security Council represented about 5 countries, whereas in 2005, one member country in Security Council represents 13 countries, thus causing a serious, asymmetrical balance. To add to their muscle, Japan and Germany since 1945 have emerged in the last sixty years as world's No.2 and No.3 largest economic super powers. Similarly, Japan feels that developing countries such as India and Brazil have carved a nice for itself by being upper tier economies on their own merits and possessing immense reservoir of manpower and skilled resources.
Third, the end of the Cold War has paved way for new issues such as international terrorism and weapons of mass destruction to come to the fore. Other sources of threat emanate from non- military threats like HIV/AIDS, poverty, environment degradation, etc and the changed global geopolitics demands concerted action and formulation of well represented deliberative decision making system in the UNSC. An enhanced permanent membership of this body, as Japan feels, will generate vigorous debate that will ultimately lead to viable solutions within the spirit of consensus building.
While the UN General Assembly is scheduled to start a debate on a draft resolution submitted by the G-4 in mid July, Japan strongly asserts that it has come out of the shadow of the past- notwithstanding the opposition to its candidacy by China and South Korea- in leading the charge from the front as the UNGA takes up the much needed reform package proposal in September and in insuring Japan's rightful place in the world's most powerful body.
Author Note
Dr. Mohammed Badrul Alam is Professor of Area Studies, Miyazaki International College, Japan
UNSC, Japan, UN, G-4


text:
Home » Festival Guide » Films » El Perro Negro: Stories from the Spanish Civil War
Hungary, Netherlands | 84 MINUTES
EL PERRO NEGRO: STORIES FROM THE SPANISH CIVIL WAR
Another mesmerizing collage assembled by the Budapest-based film and video artist, Péter Forgács, who is compiling what he terms the "private history" of 20th-century Europe. Using the work of two talented amateur filmmakers, he offers an unprecedented glimpse of Spain during the chaotic 1930s, a decade during which this Western European nation descended into barbarism. Joan Salvans was the son of a wealthy Catalan wool manufacturer, and was able to pursue his hobby of skillfully recording events in his family's life on 8mm film from 1929 until July 24, 1936, when, six days after the outbreak of the Civil War, he and his father were murdered by a militant anarchist named "Pedro el Cruel." The story line from then until the final collapse of the Loyalists and the triumph of the Falangists is taken up by the work of another young filmmaker, Ernesto Diaz Noriega, many of whose remarkable adventures, including his imprisonment and subsequent narrow escape from execution, he was able to record with his precious film camera. The personal dramas that were played out behind the scenes of this insane civil conflict add horrifying resonance to a world that we know through its portrayal by artists from Lorca to Orwell, but which we see for the first time through the suffering eyes of some of the ordinary people who were plunged into it. -Peter Scarlet "For decades the Spanish Civil War was a kind of black-and-white Robert Capa photo. It was always the ruthless Francoist and fascist bloodsuckers winning out over the left for me, the simplified vision of the fight of "good" against "evil"-nothing about the real Spain. The political clichés of the 20th-century can be dropped now, both Nazi and Soviet totalitarianism are unforgivable and unforgettable major sins." -Péter Forgács
Directed by Péter Forgács
Péter ForgÁcs was born in Budapest and studied at its Academy of Fine Arts. Influenced by the Hungarian Fluxus conceptual art movement, his work in independent film and video began at the Béla Balázs Film Studio in 1978. Forgács' own wide oeuvre includes video performances: Inauguration, Dixie & Pixie; video installations: Totem, The Visit, and documentaries: The Bartos Family, The Danube Exodus. Recipient of numerous awards and shown in all major international film and television festivals, his work can also be found in museum collections throughout Europe and the United States.


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About Cefoperazone Sodium A third generation cephalosporin antibiotic, Parenteral antibiotic. Mechanism of Action of Cefoperazone Sodium Cefoperazone is a 3rd generation cephalosporin which exerts its bactericidal action against both gram positive & gram negative organisms by inhibiting bacterial cell wall synthesis. Cefoperazone inhibits transpeptidase & thus prevents cross linking of bacterial cell wall. Transpeptidase & associated proteins constitute various types of specific binding proteins which have affinity for cephalosporins like Cefoperazone. It has greater action against pseudomonas & has good action against salmonella typhi & B.fragilis Pharmacokinets of Cefoperazone Sodium Absorption: Orally not well absorbed & should be given parenterally, Distribution: widely distributed & good CSF penetration, Metabolism: It is not significantly metabolized inside the body. Excretion: Excreted mainly through bile & a small portion is excreted in urine. A small amount is seen in breast milk.
Indications for Cefoperazone Sodium + Sulbactam SodiumIt is a combination of third generation Cephalosporins Cefperazone sodium and Beta-lactamase inhibitor Sulbactam.
1.Pain after I.M. injection & thrombophlebitis after I.V. injection.
Use with caution.Old Age Related Information May be used.Breast Feeding Related Information Use with caution.Children Related Information Use with caution.
Interactions for Cefoperazone SodiumAlcohol: Disulfiram like reaction up to 72 hours after cefoperazone intake.
Aminoglycosides (Gentamycin, Tobramycin): Nephrotoxicity of aminoglycosides potentiated.
Oral anticoagulants: Hypothrombinemic effect potentiated, bleeding complications may occur.
Bacteriostatic agents like Chloramphenicol: Decreases efficacy of Cefoperazone sodium.
Probenecid: Slows tubular excretion and this enhances efficacy.
Frusemide & Ethacrynic Acid: Potentiate nephrotoxicity of Cefoperazone Sodium.
Lab. Test: Urine Glucose: False positive with Benedicts solution and Fehlings solution.
Direct Coomb?s Test: False positive.
Urinary 17-Ketosteroid values: Falsely elevated values.
Typical Dosage for Cefoperazone Sodium Adults: 1g to 2gm I.M. or I.V. two times daily to three times daily.
Store in a refrigerator & away from light.Effects of Missed Dosage of Cefoperazone SodiumTake the missed dose as soon as noticed and if it is the time to take the next dose then skip the missed dose.
About Sulbactam SodiumAn irreversible ?-lactamase inhibitor, To increase the antibacterial activity of penicillins and cephalosporins against ?-lactamase?producing organisms.Mechanism of Action of Sulbactam SodiumSulbactam has no significant antimicrobial activity, despite sharing the ?-lactam ring that is characteristic of beta-lactam antibiotics. This similarity in chemical structure helps the Sulbactam to act as a competitive inhibitor of beta-lactamase enzyme secreted by certain bacteria that are resistant to beta-lactam antibiotics. This inhibition restores the antibacterial activity of beta-lactam antibiotics against ?-lactamase-secreting resistant bacteria.It is usually given in combination with Amoxycillin.
It is used to increase the antibacterial spectrum of penicillin and cephalosporin against penicillinase-producing and beta-lactamase-producing organisms such as Staphylococcus aureus, H. flu, Moraxella catarrhalis that are resistant to Ampicillin alone.