Regularization

Elastic Net Regression is a hybrid model that synergistically combines the strengths of Lasso and Ridge regression. It performs robust feature selection by shrinking irrelevant coefficients to zero, while also effectively handling multicollinearity by grouping correlated features. This makes it a superior and stable tool for building interpretable predictive models on complex, high-dimensional datasets commonly found in fields like genomics and finance.

Lasso Regression adds L1 regularization to linear models, shrinking some coefficients to zero and enabling feature selection. Learn how it handles overfitting and multicollinearity through controlled penalty terms and precise coefficient tuning.

Explore Ridge Regression through clear explanations and detailed math. Learn how L2 regularization helps reduce overfitting, manage multicollinearity, and improve model stability.

Regularization is a technique used in machine learning to reduce overfitting by adding constraints to the model. It improves generalization, especially in high-dimensional data, and helps balance bias and variance. Common types include L1 (Lasso), L2 (Ridge), and Elastic Net.

Bias-variance decomposition explains model error. Bias (underfitting) means a model is too simple, failing to capture data patterns. Variance (overfitting) means a model is too complex, sensitive to training data, and generalizes poorly. The goal is to balance this trade-off to minimize total prediction error for optimal model performance. Reducing bias may increase variance, and vice-versa, requiring strategic adjustments like complexity changes or regularization.

Bias is systematic error; variance is prediction variability. High bias causes underfitting; high variance causes overfitting. The bias-variance trade-off means reducing one often increases the other, making optimal model selection a key challenge. Bias is systematic error; variance is prediction variability. High bias causes underfitting; high variance causes overfitting. The bias-variance trade-off means reducing one often increases the other, making optimal model selection