Concept
Generalization error
Related lectures (123)
Addressing Overfitting in Decision Trees
Explores overfitting in decision trees and introduces random forests as a solution.
Data Representations & Processing
Explores data representations, overfitting, model selection, Bag of Words, and learning with imbalanced data.
SVM Hyperparameters
Delves into SVM hyperparameters, showcasing the impact of C and kernel width on classification results.
Gradient Descent: Early Stopping and Stochastic Gradient Descent
Explains gradient descent with early stopping and stochastic gradient descent to optimize model training and prevent overfitting.
Generalization in Deep Learning
Delves into the trade-off between model complexity and risk, generalization bounds, and the dangers of overfitting complex function classes.
Generalization Theory
Explores generalization theory in machine learning, addressing challenges in higher-dimensional spaces and the bias-variance tradeoff.
Learning Sparse Features: Overfitting in Neural Networks
Discusses how learning sparse features can lead to overfitting in neural networks despite empirical evidence of generalization.
Linear Regression and Logistic Regression
Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Optimal Regularization Strength and Learning Curves
Covers loading datasets, understanding dimensions, learning curves, and the impact of regularization on overfitting.
Decision Trees: Induction and Pruning
Explores Decision Trees, from induction to pruning, emphasizing interpretability and automatic feature selection strengths, while addressing challenges like overfitting.