Logistic regression

Formal sciences
Statistics
Data analysis
Regression analysis

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Related lectures (500)

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Explores special examples of Generalized Linear Models, covering logistic regression, count data models, separation issues, and nonparametric relationships.

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Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.

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Explores the IRLS algorithm for weighted least squares estimation in GLM.

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Covers the basics of linear regression, including OLS, heteroskedasticity, autocorrelation, instrumental variables, Maximum Likelihood Estimation, time series analysis, and practical advice.

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Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

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Covers linear models for classification, logistic regression training, evaluation metrics, and decision boundaries.

Linear Models for Classification

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Untitled

Generalization Error in Learning with Random Features

Explores generalization error in learning theory, emphasizing structured data and the replica method.

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Introduces logistic regression for binary classification and explores multiclass classification using OvA and OvO strategies.

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