Smooth Effects on Response Penalty for CLM

Your all-in-one Machine Learning resource – from scratch implementations to ensemble learning and real-world model tuning. This repository is a complete collection of 25+ essential ML algorithms written in clean, beginner-friendly Jupyter Notebooks. Each algorithm is explained with intuitive theory, visualizations, and hands-on implementation.

Library for easy deployment of A-Connect methodology.

This Jupyter Notebook demonstrates hyperparameter tuning for a Logistic Regression model using Python, with a focus on regularization techniques (L1 and L2). It explains how tuning parameters impacts model performance and helps prevent overfitting in classification tasks.

Regularization is a crucial technique in machine learning that helps to prevent overfitting. Overfitting occurs when a model becomes too complex and learns the training data so well that it fails to generalize to new, unseen data.

Classification Using Logistic Regression by Making a Neural Network Model. This project also includes comparison of Model performance when different regularization techniques are used

DUA-D2C: Dynamic Uncertainty Aware Method for Overfitting Remediation in Deep Learning

This project compares the effects of Ridge (L2) and Lasso (L1) regression models on clinical data.