🔍 Feature Selection Techniques

A Python implementation of various feature selection methods for machine learning, demonstrated using the wine dataset.

📚 Description

This repository provides a comprehensive demonstration of different feature selection techniques in machine learning. Feature selection is a crucial step in the ML pipeline that helps identify the most relevant features, reducing dimensionality while maintaining or improving model performance.

The implementation uses the wine dataset from scikit-learn as an example to showcase how different feature selection methods work and compare their results.

✨ Features

• Univariate Feature Selection: Statistical tests (Chi-squared) to select features with the strongest relationship with the output variable
• Recursive Feature Elimination (RFE): Recursively removes features and builds a model on those remaining, selecting features by recursively considering smaller sets
• Principal Component Analysis (PCA): Dimensionality reduction technique that transforms features into a new set of uncorrelated variables
• Feature Importance with Tree-based Models:
  • Extra Trees Classifier for feature ranking
  • Random Forest Classifier for feature ranking

🛠️ Prerequisites

• Python 3.6+
• Dependencies:

  pandas
  scikit-learn
  

🚀 Setup Guide

1. Clone the repository:

   git clone https://github.com/corticalstack/FeatureSelection.git
   cd FeatureSelection

2. Install the required dependencies:

   pip install pandas scikit-learn

📋 Usage

Run the main script to see all feature selection methods in action:

python main.py

📝 Resources

• Scikit-learn Feature Selection Documentation
• Feature Selection Techniques in Machine Learning
• Principal Component Analysis Explained

❓ FAQ

Q: Why is feature selection important?
A: Feature selection helps improve model accuracy, reduce overfitting, decrease training time, and enhance model interpretability by removing irrelevant or redundant features.

Q: Which feature selection method should I use?
A: It depends on your specific use case. Filter methods (like Chi-squared) are fast but don't consider feature interactions. Wrapper methods (like RFE) consider interactions but are computationally expensive. Embedded methods (like tree-based importance) offer a good balance.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.