Data Dredging

Related lectures (32)

Page 3 of 4

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

Emphasizes the importance of separating training and testing data for machine learning models.

Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

Discusses kernel methods, focusing on overfitting, model selection, and kernel functions in machine learning.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Covers the design and optimization of neural network models in deep learning.

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

Covers quantifying performance through true positives, false negatives, and false positives in machine learning.

Examines the generalization of ImageNet classifiers, safety-critical applications, overfitting, and the reliability of machine learning models.

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