Introduction

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“Feature engineering” is the process of creating, transforming, and selecting input variables (features) to improve a model's performance.

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It involves transforming raw data into features that better represent the underlying problem, making it easier for the logistic regression model to learn patterns and make accurate predictions.

Some of the goals of feature engineering in logistic regression are:

• Making features more suitable for the linear nature of logistic regression;
• Capturing important relationships and patterns in the data;
• Improving model performance and predictive accuracy; and
• Reducing noise and irrelevant information in the data.

Common techniques in logistic regression include scaling numerical features, encoding categorical variables, creating interaction terms, and handling missing values.

Content Map

To learn more about feature engineering, read each of the following sections:

Feature scaling

Interaction terms

Polynomial features

Handling categorical variables

This is the end of the topic ‘Logistic Regression’.

Next module topic:

Canonical Correlation