Performing feature selection
Feature selection is a critical step in the machine learning pipeline aimed at identifying the most relevant and informative features from the original dataset. By carefully selecting features, data scientists can improve model performance, reduce overfitting, enhance model interpretability, and decrease computational complexity.
Feature selection helps to focus a model on the most impactful features, making it more interpretable and reducing the risk of overfitting. In this section, we will explore scenarios where using all available features can lead to the “curse of dimensionality” and why selecting relevant features is crucial to mitigate this issue.
Types of feature selection
There are three main categories of feature selection techniques:
- Filter methods: These methods rank features based on statistical metrics such as correlation, mutual information, or variance. They are computationally efficient and independent of...
