Model Assessment and Hyperparameter Tuning

Related lectures (31)

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Covers the basics of polynomial regression and regularization to prevent overfitting.

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

Covers the concept of linear regression, including polynomial regression and hyperparameters selection.

Explores overfitting, regularization, and cross-validation in machine learning, emphasizing the importance of feature expansion and kernel methods.

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

Explores cross-validation, overfitting, regularization, and regression techniques in machine learning.

Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

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

Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.