Summary
A logistic regression is a versatile technique used widely in the cases where the variable to be predicted is a binary (or categorical) variable. This chapter dives deep into the math behind the logistics regression and the process to implement it using the scikit-learn and statsmodel api modules. It is important to understand the math behind the algorithm so that the model is not used as a black box without knowing what is going on behind the hood. To recap, the following are the main takeaways from the chapter:
Linear regression wouldn't be an appropriate model to predict binary variables as the predictor variables can range from -infinity to +infinity, while the binary variable would be 0 or 1.
The odds of a certain event happening is the probability of that event happening divided by the probability of that event not happening. The higher the odds, the higher are the chances of the event happening. The odds can range from 0 to infinity.
The final equation for the logistic regression...
