Feature Engineering Tutorial

Feature engineering is a crucial step in the machine learning process. It involves creating new input features or modifying existing ones to make the machine learning model more accurate. In this tutorial, we will explore the basics of feature engineering, its importance, and some common techniques.

Importance of Feature Engineering

Improves Model Performance: Proper feature engineering can significantly improve the performance of machine learning models.
Reduces Overfitting: By engineering features, we can reduce the risk of overfitting, which is when a model performs well on training data but poorly on unseen data.
Enhances Interpretability: Feature engineering can make models more interpretable, which is important for understanding the underlying patterns in the data.

Common Techniques

Feature Scaling: Standardizing the range of independent variables or features of data.
One-Hot Encoding: Converting categorical variables into a format that can be provided to machine learning algorithms to do a better job in prediction.
Polynomial Features: Creating new features that are polynomial combinations of the original features.
Interaction Features: Creating new features that represent the interaction between two or more original features.

Example

Let's say you have a dataset with two features: age and income. You can create a new feature called age_income_ratio by dividing age by income.

age_income_ratio = age / income

This new feature might help the model capture the relationship between age and income more effectively.

Feature Engineering Tutorial

Importance of Feature Engineering

Common Techniques

Example

Further Reading