#install.packages("party")
library(party)
## Loading required package: grid
## Loading required package: mvtnorm
## Loading required package: modeltools
## Loading required package: stats4
## Loading required package: strucchange
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
## Loading required package: sandwich
data("iris")
names(iris)
## [1] "Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width"
## [5] "Species"
fit2 <- ctree(Species ~ Sepal.Length + Petal.Length + Sepal.Width ,
data=iris)
plot(fit2)
print(fit2)
##
## Conditional inference tree with 4 terminal nodes
##
## Response: Species
## Inputs: Sepal.Length, Petal.Length, Sepal.Width
## Number of observations: 150
##
## 1) Petal.Length <= 1.9; criterion = 1, statistic = 140.264
## 2)* weights = 50
## 1) Petal.Length > 1.9
## 3) Petal.Length <= 4.7; criterion = 1, statistic = 61.228
## 4)* weights = 45
## 3) Petal.Length > 4.7
## 5) Petal.Length <= 5; criterion = 0.984, statistic = 7.701
## 6)* weights = 13
## 5) Petal.Length > 5
## 7)* weights = 42
nodes(fit2,1)
## [[1]]
## 1) Petal.Length <= 1.9; criterion = 1, statistic = 140.264
## 2)* weights = 50
## 1) Petal.Length > 1.9
## 3) Petal.Length <= 4.7; criterion = 1, statistic = 61.228
## 4)* weights = 45
## 3) Petal.Length > 4.7
## 5) Petal.Length <= 5; criterion = 0.984, statistic = 7.701
## 6)* weights = 13
## 5) Petal.Length > 5
## 7)* weights = 42
nodes(fit2,3)
## [[1]]
## 3) Petal.Length <= 4.7; criterion = 1, statistic = 61.228
## 4)* weights = 45
## 3) Petal.Length > 4.7
## 5) Petal.Length <= 5; criterion = 0.984, statistic = 7.701
## 6)* weights = 13
## 5) Petal.Length > 5
## 7)* weights = 42
table(Predict(fit2), iris$Species)
##
## setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 44 1
## virginica 0 6 49
#install.packages("randomForest")
library(randomForest)
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
fit3 <- randomForest(Species ~ Sepal.Length + Petal.Length + Sepal.Width ,
data=iris)
print(fit3)
##
## Call:
## randomForest(formula = Species ~ Sepal.Length + Petal.Length + Sepal.Width, data = iris)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 1
##
## OOB estimate of error rate: 7.33%
## Confusion matrix:
## setosa versicolor virginica class.error
## setosa 50 0 0 0.00
## versicolor 0 44 6 0.12
## virginica 0 5 45 0.10
importance(fit3)
## MeanDecreaseGini
## Sepal.Length 26.45171
## Petal.Length 54.09109
## Sepal.Width 15.53006
# plot(fit3)
varImpPlot(fit3)
iris$predicted.response <- predict(fit3 ,iris)
library(e1071)
#install.packages("caret")
library(caret)
## Loading required package: lattice
## Loading required package: ggplot2
##
## Attaching package: 'ggplot2'
## The following object is masked from 'package:randomForest':
##
## margin
confusionMatrix(data=iris$predicted.response,
reference=iris$Species,
positive='yes')
## Confusion Matrix and Statistics
##
## Reference
## Prediction setosa versicolor virginica
## setosa 50 0 0
## versicolor 0 50 0
## virginica 0 0 50
##
## Overall Statistics
##
## Accuracy : 1
## 95% CI : (0.9757, 1)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: setosa Class: versicolor Class: virginica
## Sensitivity 1.0000 1.0000 1.0000
## Specificity 1.0000 1.0000 1.0000
## Pos Pred Value 1.0000 1.0000 1.0000
## Neg Pred Value 1.0000 1.0000 1.0000
## Prevalence 0.3333 0.3333 0.3333
## Detection Rate 0.3333 0.3333 0.3333
## Detection Prevalence 0.3333 0.3333 0.3333
## Balanced Accuracy 1.0000 1.0000 1.0000