In this chapter, we will be continuing our journey of tackling the billion dollar problem, advertising click-through prediction. We will be focusing on learning a preprocessing technique, one-hot encoding, logistic regression algorithm, regularization methods for logistic regression, and its variant that is applicable to very large datasets. Besides the application in classification, we will also be discussing how logistic regression is used in picking significant features.

In this chapter, we will cover the following topics:

• One-hot feature encoding
• Logistic function
• The mechanics of logistic regression
• Gradient descent and stochastic gradient descent
• The training of logistic regression classifier
• The implementations of logistic regression
• Click-through prediction with logistic regression
• Logistic regression with L1 and L2 regularization
• Logistic regression for feature selection
• Online learning
• Another way to select features: random forest