Lasso (statistics)

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Related lectures (30)

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Regularization in Machine Learning

Explores Ridge and Lasso Regression for regularization in machine learning models, emphasizing hyperparameter tuning and visualization of parameter coefficients.

Sparse Regression

Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

LASSO Regression: Sparse Signal Induction

Explores LASSO regression for inducing sparsity in signals through gradient descent.

Regularization in Machine Learning

Introduces regularization techniques to prevent overfitting in machine learning models.

Overfitting in Supervised Learning: Case Studies and Techniques

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Ridge Regression: Penalised Least Squares

Explores Ridge Regression for handling multicollinearity and the LASSO method for model selection.

Regularization Techniques

Explores regularization in linear models, including Ridge Regression and the Lasso, analytical solutions, and polynomial ridge regression.

Estimation, Shrinkage and Penalization

Covers estimation, shrinkage, and penalization in statistics for data science, emphasizing the importance of balancing bias and variance in model estimation.

L1 Regularization: Sparse Solutions and Dimensionality Reduction

Delves into L1 regularization, sparse solutions, and dimensionality reduction in the context of machine learning.