1 sampling

2 ctree(conditional inference tree)

3 SVM

4 kNN

5 randomForest

6 neural networks

7 logistic regression

8 ada booting

9 naive bayes

10 gam

11 som

12 예제2

13 train

14 참고자료

7:3으로 데이터를 나눈 예제

nrow(iris)
#150

#training set sampling, test set sampling
library("caret")
partition_idx <- createDataPartition (iris$Species, p=0.3)$Resample1
training <- iris[partition_idx, ]
test <- iris[-partition_idx, ]

nrow(training); nrow(test)
#45;105

over/under sampling

library("DMwR")
resample.df <- SMOTE(Species~., data=iris, perc.over=100, perc.under=50)
table(resample.df$Species)

비율 차이가 2:1 정도 차이난다고 할 때, 데이터가 적은 쪽은 over sampling(perc.over=100)하고, 데이터가 많은 쪽은 under sampling(perc.under=50) 한다. 최근접 이웃 알고리즘을 사용하여 class를 결정한 다음 0~1사이의 random한 값을 곱하여 데이터를 추가한다고 한다.

의사결정트리(rpart 패키지)는 다음의 2가지 문제를 가지고 있다.

• over fitting
  
• 통계적 유의성을 보지 않음
  

이러한 문제를 해결한 방식이 ctree다.

library(party)
model <- ctree(Species~., data=training)
pred <- predict(model, newdata=test, type="response")

library (caret)
confusionMatrix(predict(model, newdata=test, type="response"), test$Species)

library(e1071) 
model  <- svm(Species ~ ., data = training)
pred <- predict(model, newdata=test, type="response")

library (caret)
confusionMatrix(pred, test$Species)
table(pred, test$is_out)

obj <- tune.svm(factor(is_out)~., data = training, sampling = "fix", gamma = 2^c(-8,-4,0,4), cost = 2^c(-8,-4,-2,0))
plot(obj, transform.x = log2, transform.y = log2)
plot(obj, type = "perspective", theta = 120, phi = 45)

library("kernlab")
model <- ksvm(factor(is_out) ~., data=training, kernel = "rbfdot")
pred <- predict(model, newdata=test2, type="response")             
confusionMatrix(pred, test2$is_out)     

library("class") 
pred <- knn(training[,1:4], test[,1:4], training$Species, k = 5, prob=TRUE)

library (caret)
confusionMatrix(pred, test$Species)

library("randomForest")
model <- randomForest(Species ~ ., data=training, type="classification", importance=TRUE)
pred <- predict(model, newdata=test)

library (caret)
confusionMatrix(pred, test$Species)

library(nnet)
model <- nnet(Species~., data=training, size=5)
pred <- predict(model, newdata=test, type="class")

library (caret)
confusionMatrix(pred, test$Species)

library(nnet)
model <- multinom(Species~., data=training)
#head (fitted(out)) #결과는 확률
pred <- predict(model, newdata=test, type="class")

library (caret)
confusionMatrix(pred, test$Species)

summary(model)

> summary(model)
Call:
multinom(formula = Species ~ ., data = training)

Coefficients:
           (Intercept) Sepal.Length Sepal.Width Petal.Length Petal.Width
versicolor    157.4558    -45.15929   -27.99315     79.58368   -58.51255
virginica    -157.1791     13.78356   -82.12478     54.58758    80.74923

Std. Errors:
           (Intercept) Sepal.Length Sepal.Width Petal.Length Petal.Width
versicolor    17101.14     9088.735    10475.50     4091.601    7197.552
virginica     17101.14     9088.740    10475.49     4091.600    7197.552

Residual Deviance: 0.0001210002 
AIC: 20.00012 
> 

library(ada)
model <- ada(Species~., data=training)
pred <- predict(model, newdata=test)

library (caret)
confusionMatrix(pred, test$Species)

library(e1071)
model <- naiveBayes(Species~., data=training)
pred <- predict(model, newdata=test)

library (caret)
confusionMatrix(pred, test$Species)

library("mgcv")
model <- gam(이탈여부 ~ s(변수1) + s(변수2),family=binomial, data=training)
summary(model)
pred <- predict(model, test, type="response")
confusionMatrix(ifelse(pred < 0.5, "이탈", "잔존"), test$이탈여부)

library("kohonen")
training.class <- training$Species
test.class <- test$Species
training <- scale(training[,1:4])
test <- scale(test[,1:4])

model <- som(training, grid = somgrid(5, 5, "hexagonal"))
pred <- predict(model, newdata = test,
                          trainX = training,
                          trainY = factor(training.class))

confusionMatrix(pred$prediction, test.class)  

#ctree
library(party)
library (caret)
model <- ctree(factor(is_out)~., data=training)
#model <- ctree(factor(is_out)~., data=training, weights=ifelse(training$누적구매건수 >= 0, 100, 1))
pred <- predict(model, newdata=test, type="response")
confusionMatrix(predict(model, newdata=test, type="response"), test$is_out)

#randomForest
library(randomForest) 
model <- randomForest(factor(is_out) ~ ., data=training, type="classification", importance=TRUE, proximity=TRUE)
pred <- predict(model, newdata=test)
confusionMatrix(pred, test$is_out)
imp <- data.frame(importance(model))
imp[order(imp$MeanDecreaseGini, decreasing=T),]
varImpPlot(model)

#CART
library(rpart)
model <- rpart(factor(is_out) ~., data=training, method="class")
pred <- predict(model, newdata=test, type="class")
confusionMatrix(pred, test$is_out)

#SVM
library(e1071) 
model <- svm(factor(is_out) ~., data=training, method="class")
pred <- predict(model, newdata=test, type="class")
confusionMatrix(pred, test$is_out)

#NN
library(nnet)
model <- nnet(factor(is_out) ~., data=training, size=40, method="class")
pred <- predict(model, newdata=test, type="class")
confusionMatrix(pred, test$is_out)

#kNN
library("class") 
pred <- knn(training[,2:ncol(training)], test[,2:ncol(training)], training$is_out, k = 7, prob=TRUE)
confusionMatrix(pred, test$is_out)

#logistic regression
library(nnet)
model <- multinom(is_out~., data=training)
pred <- predict(model, newdata=test, type="class")
confusionMatrix(pred, test$is_out)

library(caret)
library(rpart)
library(e1071)

data(iris)
formula <- as.formula(Species ~.)
t <- train(formula, iris, method = "rpart", cp=0.002, maxdepth=8)
plot(t)


plot(t$finalModel)
text(t$finalModel)

library(rattle)
library("rpart.plot")
fancyRpartPlot(t$finalModel)

• http://www.slideshare.net/kmettler/caret-package-for-r
  
• http://r4pda.co.kr/r4pda_2013_12_01.pdf