Binary Classification Cost Function

Related lectures (30)

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Explores binary classification by regression, decision functions, and various cost functions.

Covers supervised learning, binary and multi-class classification problems, and making predictions from labeled examples.

Covers linear models for multi-class classification, focusing on logistic regression and evaluation metrics.

Covers the formalism for supervised learning and decision functions in classification problems.

Covers logistic regression for binary classification using probability modeling and optimization methods.

Explores supervised learning in asset pricing, focusing on stock return prediction challenges and model assessment.

Explores supervised learning in financial econometrics, emphasizing classification algorithms like Naive Bayes and Logistic Regression.

Explores Generalized Linear Regression, Classification, confusion matrices, ROC curves, and noise in data.

Covers the parameterization of lines in 2D space and the perceptron algorithm.

Introduces decision trees and k-nearest neighbors for classification tasks, exploring metrics like accuracy and AUC.