Lasso and Inductive Conformal Prediction Algorithm

Overview

This repository contains Python code implementing Lasso regression and an Inductive Conformal Predictor for predicting diabetes-related outcomes. The code utilizes the scikit-learn library for Lasso regression and inductive conformal prediction principles.

Files

• lasso_diabetes.py: Python script implementing Lasso regression on the diabetes dataset.
• lasso_diabetes_tabsep.py: Python script implementing Lasso regression on a tab-separated diabetes dataset.
• conformal_prediction.py: Python script implementing the Inductive Conformal Predictor.
• README.md: Overview of the repository and usage instructions.

Instructions

1. Lasso Regression for diabetes dataset

   • Load the diabetes dataset using load_diabetes from sklearn.
   • Split the dataset into training and testing sets.
   • Train Lasso models with different alpha values and assess their performance.
   • Evaluate the number of selected features and their impact on test R2.
   • Visualize the relationship between test R2 and the number of features.

2. Lasso Regression for tab-separated diabetes dataset

   • Read the tab-separated diabetes dataset.
   • Split the dataset into training and testing sets.
   • Train Lasso models with different alpha values and evaluate their performance.
   • Assess the number of selected features and their impact on test R2.
   • Visualize the relationship between test R2 and the number of features.

3. Standard Scaling and Cross-validation

   • Preprocess the training and test sets using StandardScaler.
   • Choose the regularization parameter for Lasso using cross-validation on the training set.

4. Inductive Conformal Predictor

   • Split the original training data into new training and calibration data.
   • Preprocess data using StandardScaler.
   • Find the best parameter for Lasso and predict preprocessed calibration data.
   • Calculate non-conformity measures and define significance levels.
   • Predict for scaled test data and calculate prediction intervals.
   • Assess error rates for predicted labels at different significance levels.

Dependencies

• Python3
• scikit-learn
• pandas
• matplotlib
• numpy

How to Use

1. Clone the repository:

   git clone https://github.com/your_username/lasso-conformal-prediction.git

2. Install dependencies:

   pip install scikit-learn pandas matplotlib numpy

3. Run the Python scripts as needed:

   python Lasso_and_Inductive_Conformal_Prediction_Algorithm.ipynb

Feel free to explore and modify the code to suit your specific needs. For more details, refer to the comments within each script.