Logistic Regression: Statistical Inference and Machine Learning

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Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

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

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

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.

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

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

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

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explores logistic regression for binary response variables, covering topics such as odds ratio interpretation and model fitting.