Overfitting

Related lectures (29)

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Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.

Covers polynomial regression, gradient descent, overfitting, underfitting, regularization, and feature scaling in optimization algorithms.

Explores Mixture of Gaussians, Bayes' classification, kNN, overfitting, and computational costs.

Covers linear and logistic regression, including underfitting, overfitting, and performance metrics.

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.

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

Discusses the bias-variance tradeoff in machine learning, emphasizing the balance between model complexity and prediction accuracy.

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

Introduces the basics of supervised machine learning, covering types, techniques, bias-variance tradeoff, and model evaluation.