Question: I currently have a background fixed in my body tag

```
body
{
background-image: url("http://i.imgur.com/t3bikz8.jpg");
-webkit-background-size: cover;
-moz-background-size: cover;
background-size: cover;
background-attachment: fixed;
}

```

and i am also using "smoothscroll" feature, and i was hoping someone could help me out with 2 queries i have..

1) how do i make it that my background image only stays at the top of the page when you load, so it does not follow you down as you scroll.

2) secondly, i was hoping that the user would not be able to begin scrolling, unless they clicked one of my 4 available buttons on my main start up page. (i am bot sure is this command relevant to the background or not, sorry if its not)

thanks

hoping it will function like this background

<http://www.andreagalanti.it/flatfy/>

Answer: 
This is now available.

Spark ML provides:

* a **`predictionCol`** that contains the **predicted label**
* and a **`probabilityCol`** that contains a vector with the **probabilities for each label**, *this is what you where looking for*!
* You can also access the raw counts

For more details, here is the Spark Documentation:
<http://spark.apache.org/docs/latest/ml-classification-regression.html#output-columns-predictions>
Probably people would have moved on with this post, but i was hit by the same problem today when trying to compute the accuracy for the multi-class classifier against a training set. So I thought I share my experience if someone is trying with mllib ...

probability can be computed fairly easy as follows:-

```
# say you have a testset against which you want to run your classifier
   (trainingset, testset) =data.randomSplit([0.7, 0.3])
   # I converted the spark dataset containing the test data to pandas
     ptd=testData.toPandas()

   #Now get a count of number of labels matching the predictions

   correct = ((ptd.label-1) == (predictions)).sum() 
   # here we had to change the labels from 0-9 as opposed to 1-10 since
   #labels take the values from 0 .. numClasses-1

   m=ptd.shape[0]
   print((correct/m)*100)

```