Variable Selection Methods

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Related lectures (31)

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Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Overfitting in Supervised Learning: Case Studies and Techniques

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

Linear Regression: Statistical Inference and Regularization

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

Supervised Learning: Regression Methods

Explores supervised learning with a focus on regression methods, including model fitting, regularization, model selection, and performance evaluation.

Statistical Inference: Model Selection and Nuisance Parameters

Covers model selection, nuisance parameters, and higher-order inference methods in statistical inference.

Model Selection: Least Squares

Explores model selection in least squares regression, addressing multicollinearity challenges and introducing shrinkage techniques.

Model Building: Linear Regression

Explores model building in linear regression, covering techniques like stepwise regression and ridge regression to address multicollinearity.

The Information Criteria of Takeuchi and Akaike

Covers the estimation of optimism for predictors via statistical models and the application of AIC to linear regression.

Linear Regression and Logistic Regression

Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.