Jupyter Notebook Feature Selection Guide with Python

This repository provides a collection of Jupyter Notebook examples demonstrating various feature selection techniques using Python. Feature selection is a crucial step in machine learning that involves selecting the most relevant features from a dataset to improve model performance, reduce overfitting, and enhance interpretability.

Feature Selection Techniques

The following feature selection techniques are covered in this guide:

1. Univariate Selection

   • Univariate selection evaluates each feature independently and selects the best features based on statistical tests such as chi-squared or ANOVA. It assumes that each feature is independent of others.
   • The guide provides a Jupyter Notebook example using scikit-learn's SelectKBest and chi-squared test to demonstrate univariate selection.

2. Recursive Feature Elimination (RFE)

   • RFE is a recursive technique that starts with all features, builds a model, and gradually removes the least significant features based on their importance. It repeatedly fits the model and prunes features until a specified number of features remain.
   • The guide includes a Jupyter Notebook example using scikit-learn's RFE class to showcase the RFE feature selection technique.

3. Recursive Feature Addition (RFA)

   • Recursive Feature Addition (RFA) is a feature selection technique that adds features one by one based on their importance.
   • It starts with an empty set of features and gradually incorporates the most significant feature at each iteration.
   • RFA is useful for identifying the most informative features in datasets with a large number of features.

4. L1-based Feature Selection (Lasso)

   • L1-based feature selection, such as Lasso, utilizes regularization to encourage sparsity in the feature coefficients. It assigns small or zero coefficients to irrelevant features, effectively removing them from the model.
   • The guide provides a Jupyter Notebook example using scikit-learn's Lasso class to demonstrate L1-based feature selection.

5. Mutual Information

   • Mutual Information is a statistical measure that quantifies the dependence between two variables.
   • It is used in feature selection to evaluate the relationship between each feature and the target variable.

6. Hierarchical Voting

   • It combines multiple feature selection algorithms, assigning scores to features and aggregating them for a final determination of important features. It is a robust approach for complex datasets.

7. Boruta

   • Boruta is a feature selection technique that uses random forests to determine feature importance. It compares each feature's importance with randomly generated shadow features to identify significant features.

Each technique is accompanied by code examples. The Jupyter Notebook files are interactive and allow you to experiment with the code and explore the impact of different parameters.

Getting Started

To get started with the feature selection guide, follow these steps:

1. Clone the repository:

   git clone https://github.com/trizkynoviandy/feature-selection-guide.git
   

2. Install the required dependencies. You can use pip or conda to install the necessary

3. Navigate to the guide folder and open the Jupyter Notebook files (.ipynb) using Jupyter

4. Follow along with the guide notebooks and executing the code cells.

References

Here are some additional resources for further reading on feature selection:

• Scikit-learn Feature Selection
• Xverse
• Feature Engine

These resources provide additional insights, explanations, and variations of feature selection techniques that you can explore to deepen your understanding.