Cross-validation (statistics)

Related lectures (32)

Page 3 of 4

Covers linear and ridge regression, overfitting, hyperparameters, and test sets.

Covers sampling, cross-validation, quantifying performance, optimal model determination, overfitting detection, and classification sensitivity.

Delves into NLP evaluation, covering gold standards, precision, recall, and statistical significance.

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

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

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

Explores regularization methods in neural networks, emphasizing the importance of training and validation bases to prevent overfitting.

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.

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

Explores overfitting and accuracy measures in image classification, emphasizing the importance of model generalization and optimal accuracy.