Regression
model1 <- lm(creditAD$Years of Credit History ~ creditAD$Credit Score) summary(model1)
model1$coefficients[2] <--0.095 model1$coefficients[1] <--74.66 summary(model1)
plot(creditAD$Years of Credit History~creditAD$Credit Score) abline(model1, col="red")
creditAD$`yoc_pred <- predict(model1, newdata = creditAD)
creditAD$klas2 <- ifelse(creditAD$Years of Credit History < creditAD$yoc_pred,0,1) creditAD$`yoc_pred <-predict(model1, newdata = creditAD)
creditAD$klas2 <- ifelse(creditAD$Years of Credit History < creditAD$yoc_pred, 0, 1) creditAD$klas2 <- ifelse(creditAD$Years of Credit History < creditAD$yoc_pred,0,1) creditAD$yoc_pred <-predict(model1, newdata = creditAD)
creditAD$klas2 <- ifelse(creditAD$Years of Credit History < creditAD$yoc_pred,0,1) creditAD$klas2
creditAD$klas1 <- ifelse(creditAD$klas1==0 , "Reject", "Approve") table(creditAD$Decision, creditAD$klas1)
creditad
creditAD$mklas1 <- ifelse(creditAD$klas1==1, "Approve", "Reject") crosstab <- table(creditAD$Decision, creditAD$mklas1)
probmisklas <- 1 - sum(diag(crosstab))/ sum(crosstab) probmisklas
plot(creditAD$Years of Credit History~ creditAD$Credit Score, pch=creditAD$mklas2) plot(creditAD$Years of Credit History~creditAD$Credit Score, pch = creditAD$mklas2) plot(creditAD$Years of Credit History~creditAD$Credit Score, pch = creditAD$klas2) abline(model1, col ="red")
creditad
creditAD$mklas2 <- ifelse(creditAD$klas2==1, "Approve", "Reject")
plot(creditAD$Years of Credit History~ creditAD$Credit Score, pch = creditAD$mklas2) abline(model1, col ="red")
creditAD$mklas2a <- ifelse(creditAD$mklas2==1,"Approve", "Reject") crosstab <- table(creditAD$Decision, creditAD$mklas2a)
probmisklas <- 1 - sum((diag(crosstab))/sum(crosstab) probmisklas <- 1 - sum(diag(crosstab))/sum(crosstab) probmisklas <- 1 - sum(diag(crosstab))/sum(crosstab)
Decision tree
library(party)
install.packages("party") library(party)
head(readingSkills) dim(readingSkills)
n <- round(nrow(readingSkills)*0.80);n set.seed(123)
n <- round(nrow(readingSkills)*0.80) n
sam <- sample(1;nrow(readingSkills),n) sam <- sample(1:nrow(readingSkills),n) data.train <- readingSkills[samp,]
sam
data.train <- readingSkills[sam,]
dim(data.train) names(data.train)
data.test <- readingSkills[-sam,]
dim(data.test)
fit <- rpart(nativeSpeaker~.,data = data.train, method = class) fit <- rpart(nativeSpeaker~.,data = data.train, method = class)
library(rpart)
fit <- rpart(nativeSpeaker~.,data = data.train, method = class) library(rpart.plot)
install.packages(rpart.plot) install.packages("rpart.plot")
fit <- rpart(data.train$nativeSpeaker, data = data.train, method = class) fit <- rpart(data.train$nativeSpeaker, data = data.train, method=class) fit <- rpart(data.train$nativeSpeaker~., data = data.train, method=class) fit <- rpart(data.train$nativeSpeaker~., data = data.train, method="class") rpart.plot(fit)
