Least-angle regression

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Explores quantile regression for electricity price prediction using time series data, regularization, and the kernel trick.

Explores treatment effects, confounding variables, and challenges in interpreting observational study results.

Explains the importance of training, validation, and test sets in machine learning phases.

Explores the geometric explanation of why Lasso solutions are sparse and how coefficients change with the regularization parameter.

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

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

Delves into the challenges of using standard ML for stable robot control.

Covers linear regression coefficients, confidence intervals, and R's calculation methods.

Covers the concept of Singular Value Decomposition and its application in regression techniques.

Compares L1 and L0 penalization in linear regression with orthogonal designs using greedy algorithms and empirical comparisons.