Logistic Regression

Introduction

Logistic regression models the probability of a binary outcome. It's used for classification problems.

Fitting Logistic Regression

# Fit model
model <- glm(y ~ x, data = df, family = "binomial")

# Model summary
summary(model)

# Coefficients
coef(model)
exp(coef(model))  # Odds ratios

Predictions

# Predicted probabilities
predict(model, type = "response")

# Class predictions
pred <- predict(model, type = "response") > 0.5

Model Evaluation

# Confusion matrix
library(caret)
confusionMatrix(pred, actual)

# ROC curve
library(pROC)
roc(actual, predicted_probabilities)

Multiple Predictors

model <- glm(y ~ x1 + x2 + x3, 
             data = df, 
             family = "binomial")

Summary

Logistic regression is essential for binary classification. Interpret coefficients as odds ratios.