Logistic Regression, Stochastic Gradient, Regularization.
predict. Example: made up data Here we have 4 predictors or features and y is our target that we intend to predict 0 or 1. Here’s the log loss equation: σ is the sigmoid function, w is the weight vector we need to derive, bi is the bias or y-intercept and xi is the datapoint with d dimensions where