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# Logistic Regression

## Estimating the Regression Coefficients

The estimates \(\beta_0\) and \(\beta_1\) are chosen to maximize the likelihood function. MLE p133

In the linear regression setting, the least squares approach is in fact a special case of **maximum likelihood**.

## Making Predictions

# Multiple Logistic Regression

## Logistic Regression for >2 Response Classes

# Linear Discriminant Analysis - LDA

## Using Bayes’ Theorem for Classification

This is a generative model

## Linear Discriminant Analysis for p = 1

## Linear Discriminant Analysis for p > 1

confusion matrix p145

sensitivity and specificity

ROC curve

# Quadratic Discriminant Analysis - QDA

Since the Bayes decision boundary is linear, it is more accurately approximated by LDA than by QDA p150

# A Comparison of Classification Methods