3. Feature Selection#
Feature selection is the process of choosing the most relevant features for a machine learning model and removing irrelevant or redundant variables.
Reducing unnecessary features can help:
improve model performance
reduce overfitting
decrease training time
make models easier to interpret
Example#
Original features:
Size |
Bedrooms |
Zip Code |
Price |
|---|
If Zip Code does not contribute to predicting house price, it may be removed.
Selected features:
| Size | Bedrooms | Price |
Common Approaches#
Method |
Description |
|---|---|
Filter Methods |
Use statistical measures such as correlation to select features |
Wrapper Methods |
Evaluate different feature combinations using a model |
Embedded Methods |
Feature selection occurs during model training (e.g., Lasso, decision trees) |