Concept

Model selection

Related lectures (32)

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

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.

Maximum Likelihood: Inference and Model Comparison

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Model Selection: Non-Nested Model Selection

Explores model selection, criteria, bias/variance tradeoff, and cross-validation methods.

Statistical Inference: Model Selection and Nuisance Parameters

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

Model Selection Methods in Biostatistics

Explores model selection methods in biostatistics, emphasizing the importance of starting with a sensible model.

Inference for Max-Stable Processes

Covers the inference steps for max-stable processes and the fitting of extreme rainfall models.

Model Selection: AIC and BIC

Explores model selection using AIC and BIC criteria, addressing different questions and the importance of sparsity in selecting the best model.

Model Selection

Covers model selection, emphasizing the tradeoff between complexity and error in data analysis.

Risk Estimation: Mallows' CL and Cp

Discusses optimism in risk estimation, effective degrees of freedom, and Mallows' CL and Cp for linear estimators.

Discrete Choice Analysis

Explores the integration of machine learning into discrete choice models, emphasizing the importance of theory constraints and hybrid modeling approaches.

Discrete choice and machine learning: two methodologies

Delves into the complementary methodologies of discrete choice and machine learning, covering notations, variables, models, data processes, extrapolation, what-if analysis, and more.

Avoiding Implicit Bias in Hiring

Discusses blind auditions, selection criteria, and recommendation letters in hiring to prevent gender bias.

Bayesian Parameter Estimation

Explores Bayesian Parameter Estimation, emphasizing cost minimization and reward maximization.

Generalized Linear Models

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Variable Selection Methods

Explores variable selection methods and the bias-variance trade-off in statistical modeling.

Model Selection and Evaluation: Bias-Variance Dilemma, Ridge Estimation

Explores over-learning, generalization, and under-learning in machine learning models.

Model Selection: Generalization and Validation

Explores generalization, model selection, and validation in machine learning, emphasizing the importance of unbiased model evaluation.