Today we will breifly discuss Regression methods and the use of Regression for Classification.

• Linear Regression/Multiple Linear Regression
• Logistic Regression
• Regression Trees
• CART

Having taken a Regression class you know about

• Linear Regression
• Multiple Regression

What about???

• Logistic Regression
• Poisson Regression
• Generalized Linear Models (GLMs)

The main idea with Regression is to model the relationship between a dependent variable and an independent variable(s).

To make numeric predictions.

The main idea with Logisitic Regression is to model the relationship between a 0-1 dependent variable and an independent variable(s).

To make classifications.

Read over the first half of Chapter 6, this is review.

We will try the predicting medical expenses example.

In R the lm() function is used to fit linear regression models it knows about dummy variables. There is no extra work that is need to include categorical variables into a regression model. This is because when a categorical variable is a factor in R, the lm() function knows the dummy variables to use.

Lantz: See pages 180, 181 / 194, 195

The preceding Chapter, Trees were used for Classification.

Later in this Chapter, Trees are used for Numeric Prediction.

One type of tree for prediction is CART, Classification and Regression Trees.

This is a bit of a misnomer, Linear Regresion methods are not used. Predictions are made based on the average value of examples that reach a leaf.

A second type of tree for prediction is known as Model Trees.

These were developed later, are less widely used but may be more powerful.

A multiple linear regression model is built from the exmples reaching that node.

Trees can make predictions and can be considered as an alternative to regression modeling.

The data are partitioned using a divide-and-conquer strategy according to the feature that will result in the greatest increase in homogeneity in the outcome after a split is performed.

For Classification Trees entropy is used.

For Numeric Decision Trees statistics such as standard deviation are used.

Today we will fit a multiple linear regression model for the insurance data.

We will look at the application of Regression Trees to the wine rating data.

The rpart package will be used.