# Preprocess iris to create a binary case 
iris_mod <- iris
iris_mod <- dplyr::mutate(iris_mod, is_setosa = as.numeric(Species=='setosa'))[-5]
print(iris_mod)

# Split train, test
ind <- sample(nrow(iris_mod),0.8*nrow(iris_mod))
train <- iris_mod[ind,]
test <- iris_mod[-ind,]

# Use glm function to predict species just with Sepal.Width
fit_logit <- glm(data=train,family=binomial,formula = is_setosa ~ Sepal.Width)
print(summary(fit_logit))

# predict using predict
pred_logit <- predict(object = fit_logit, newdata = test)
# use roc function in pROC library
library(pROC)
roc_curve <- roc(predictor = pred_logit, response=test$is_setosa )
plot(roc_curve)
auc(curve)

auc(roc_curve)

install.packages("rpart")
library(rpart)
install.packages("rpart.plot")
library(rpart.plot)
TrainCourt = read.csv("resources/TrainCourt.csv")
TestCourt = read.csv("resources/TestCourt.csv")

SupremeCourtTree = rpart(Reverse ~ Circuit + Issue + Petitioner + Respondent + LowerCourt + 
                         Unconst, data = Train, method="class", minbucket=25)

prp(SupremeCourtTree)

PredictCART = predict(SupremeCourtTree, newdata = TestCourt, type = "class")