Logistic Regression

Related lectures (31)

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Introduces the fundamentals of supervised learning, including loss functions and probability distributions.

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

Covers supervised learning, classification using logistic regression, and challenges in optimization.

Explores linear models for classification, including binary classification, logistic regression, decision boundaries, and support vector machines.

Covers the concept of multiclass classification and the challenges of linearly separating data with multiple classes.

Explores linear models for classification, logistic regression, SVM, k-NN, and curse of dimensionality.

Explores linear models for classification, including logistic regression, decision boundaries, and support vector machines.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

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

Covers linear models for classification, focusing on logistic regression and support vector machines.