LR for churn prediction

LR is one of the most widely used classifiers to predict a binary response. It is a linear ML method, as described in Chapter 1, Analyzing Insurance Severity Claim. The loss function is the formulation given by the logistic loss:

For the LR model, the loss function is the logistic loss. For a binary classification problem, the algorithm outputs a binary LR model such that, for a given new data point, denoted by x, the model makes predictions by applying the logistic function:

In the preceding equation, z = W^TX and if f(W^TX)>0.5, the outcome is positive; otherwise, it is negative.

Note

Note that the raw output of the LR model, f(z), has a probabilistic interpretation.

Note that compared to linear regression, logistic regression provides you with a higher classification accuracy. Moreover, it is a flexible way to regularize a model for custom adjustment, and overall, the model responses are measures of probability.

Most importantly, whereas linear regression can predict...