Generalization and Overfitting

Related lectures (31)

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Explores model assessment, hyperparameter tuning, and resampling strategies in machine learning.

Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.

Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.

Covers polynomial regression, flexibility impact, and underfitting vs overfitting.

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Introduces the basics of machine learning, covering supervised classification, decision boundaries, and polynomial curve fitting.

Explores the Gaussian Discriminant Rule for classification using Gaussian Mixture Models and discusses drawing boundaries and model complexity.

Explores overfitting in polynomial regression, emphasizing the importance of generalization in machine learning and statistics.

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

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