Feature Selection

What is Feature Selection?

Feature selection is the process of choosing the most important columns (features) in your data to use for building a machine learning model. It helps make models simpler, faster, and sometimes more accurate.

Why is Feature Selection Important?

Removes unnecessary or noisy data
Makes models easier to understand
Can improve model performance
Reduces computation time

Main Types of Feature Selection

Method	How it Works	Speed	Uses Model?	Example Techniques
Filter	Uses stats to pick features	Fast	No	Correlation, Variance
Wrapper	Tries feature sets with a model	Slow	Yes	RFE, Forward Selection
Embedded	Picks features during model training	Medium	Yes	Lasso, Decision Trees

Filter: Uses statistics to select features before modeling.
Wrapper: Tries different feature sets with a model to find the best.
Embedded: The model itself selects features as it learns.

When Should You Use Feature Selection?

When you have lots of features (columns)
When you want a simpler or faster model
When you want to avoid overfitting

The Feature Selection Process

Learn more about each method in the pages below!