Regression Dr. Jieh-Shan George YEH

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Presentation transcript:

Regression Dr. Jieh-Shan George YEH

ARULES: MINING ASSOCIATION RULES AND FREQUENT ITEMSETS

Regression Regression is to build a function of independent variables (also known as predictors) to predict a dependent variable (also called response). For example, banks assess the risk of home- loan applicants based on their age, income, expenses, occupation, number of dependents, total credit limit, etc.

Linear Regression Linear regression is to predict response with a linear function of predictors as follows: y = c 0 + c 1 x 1 + c 2 x c k x k ; where x 1 ; x 2 ;… ; x k are predictors and y is the response to predict.

year <- rep(2008:2010, each=4) quarter <- rep(1:4, 3) cpi <- c(162.2, 164.6, 166.5, 166.0, , 167.0, 168.6, 169.5, , 172.1, 173.3, 174.0) plot(cpi, xaxt="n", ylab="CPI", xlab="") # draw x-axis axis(1, labels=paste(year,quarter,sep="Q"), at=1:12, las=3)

library(scatterplot3d) s3d <- scatterplot3d(year, quarter, cpi, highlight.3d=T, type="h", lab=c(2,3)) s3d$plane3d(fit)

data2011 <- data.frame(year=2011, quarter=1:4) cpi2011 <- predict(fit, newdata=data2011) style <- c(rep(1,12), rep(2,4)) plot(c(cpi, cpi2011), xaxt="n", ylab="CPI", xlab="", pch=style, col=style) axis(1, at=1:16, las=3,labels=c(paste(year,quarter,sep="Q"), "2011Q1", "2011Q2", "2011Q3", "2011Q4"))

Logistic Regression Logistic regression is used to predict the probability of occurrence of an event by fitting data to a logistic curve. A logistic regression model is built as the following equation:

Generalized Linear Regression The generalized linear model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function and allowing the magnitude of the variance of each measurement to be a function of its predicted value.

Generalized Linear Regression It unifies various other statistical models, including linear regression, logistic regression and Poisson regression. Function glm() is used to t generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the error distribution.

data("bodyfat", package="TH.data") myFormula <- DEXfat ~ age + waistcirc + hipcirc + elbowbreadth + kneebreadth bodyfat.glm <- glm(myFormula, family = gaussian("log"), data = bodyfat) summary(bodyfat.glm) pred <- predict(bodyfat.glm, type="response")

plot(bodyfat$DEXfat, pred, xlab="Observed Values", ylab="Predicted Values") abline(a=0, b=1)

Non-linear Regression non-linear regression is to fit a curve through data.