breathes, venomous, fins, legs, tail, domestic, catsize, type
#install.packages("pROC")
#install.packages("mlbench")
#install.packages("mlbench")
library(caret)
## Warning: package 'caret' was built under R version 3.5.1
## Loading required package: lattice
## Loading required package: ggplot2
## Warning: package 'ggplot2' was built under R version 3.5.1
library(pROC)
## Warning: package 'pROC' was built under R version 3.5.1
## Type 'citation("pROC")' for a citation.
##
## Attaching package: 'pROC'
## The following objects are masked from 'package:stats':
##
## cov, smooth, var
library(mlbench)
## Warning: package 'mlbench' was built under R version 3.5.1
library(lattice)
# Example 1 - Glass dataset (Classification)
zoo <- read.csv(file.choose())
#glass$Type[glass$Type==1] <- 'Type1'
# glass$Type[glass$Type==2] <- 'Type2'
# glass$Type[glass$Type==3] <- 'Type3'
# glass$Type[glass$Type==4] <- 'Type4'
# glass$Type[glass$Type==5] <- 'Type5'
# glass$Type[glass$Type==6] <- 'Type6'
# glass$Type[glass$Type==7] <- 'Type7'
str(zoo)
## 'data.frame': 101 obs. of 18 variables:
## $ animal.name: Factor w/ 100 levels "aardvark","antelope",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ hair : int 1 1 0 1 1 1 1 0 0 1 ...
## $ feathers : int 0 0 0 0 0 0 0 0 0 0 ...
## $ eggs : int 0 0 1 0 0 0 0 1 1 0 ...
## $ milk : int 1 1 0 1 1 1 1 0 0 1 ...
## $ airborne : int 0 0 0 0 0 0 0 0 0 0 ...
## $ aquatic : int 0 0 1 0 0 0 0 1 1 0 ...
## $ predator : int 1 0 1 1 1 0 0 0 1 0 ...
## $ toothed : int 1 1 1 1 1 1 1 1 1 1 ...
## $ backbone : int 1 1 1 1 1 1 1 1 1 1 ...
## $ breathes : int 1 1 0 1 1 1 1 0 0 1 ...
## $ venomous : int 0 0 0 0 0 0 0 0 0 0 ...
## $ fins : int 0 0 1 0 0 0 0 1 1 0 ...
## $ legs : int 4 4 0 4 4 4 4 0 0 4 ...
## $ tail : int 0 1 1 0 1 1 1 1 1 0 ...
## $ domestic : int 0 0 0 0 0 0 1 1 0 1 ...
## $ catsize : int 1 1 0 1 1 1 1 0 0 0 ...
## $ type : int 1 1 4 1 1 1 1 4 4 1 ...
zoo1 <- zoo[,2:18]
str(zoo1)
## 'data.frame': 101 obs. of 17 variables:
## $ hair : int 1 1 0 1 1 1 1 0 0 1 ...
## $ feathers: int 0 0 0 0 0 0 0 0 0 0 ...
## $ eggs : int 0 0 1 0 0 0 0 1 1 0 ...
## $ milk : int 1 1 0 1 1 1 1 0 0 1 ...
## $ airborne: int 0 0 0 0 0 0 0 0 0 0 ...
## $ aquatic : int 0 0 1 0 0 0 0 1 1 0 ...
## $ predator: int 1 0 1 1 1 0 0 0 1 0 ...
## $ toothed : int 1 1 1 1 1 1 1 1 1 1 ...
## $ backbone: int 1 1 1 1 1 1 1 1 1 1 ...
## $ breathes: int 1 1 0 1 1 1 1 0 0 1 ...
## $ venomous: int 0 0 0 0 0 0 0 0 0 0 ...
## $ fins : int 0 0 1 0 0 0 0 1 1 0 ...
## $ legs : int 4 4 0 4 4 4 4 0 0 4 ...
## $ tail : int 0 1 1 0 1 1 1 1 1 0 ...
## $ domestic: int 0 0 0 0 0 0 1 1 0 1 ...
## $ catsize : int 1 1 0 1 1 1 1 0 0 0 ...
## $ type : int 1 1 4 1 1 1 1 4 4 1 ...
zoo1$hair <- as.factor(zoo1$hair)
zoo1$feathers <- as.factor(zoo1$feathers)
zoo1$eggs <- as.factor(zoo1$eggs)
zoo1$milk <- as.factor(zoo1$milk)
zoo1$airborne <- as.factor(zoo1$airborne)
zoo1$aquatic <- as.factor(zoo1$aquatic)
zoo1$predator <- as.factor(zoo1$predator)
zoo1$toothed <- as.factor(zoo1$toothed)
zoo1$backbone <- as.factor(zoo1$backbone)
zoo1$breathes <- as.factor(zoo1$breathes)
zoo1$venomous <- as.factor(zoo1$venomous)
zoo1$fins <- as.factor(zoo1$fins)
zoo1$legs <- as.factor(zoo1$legs)
zoo1$tail <- as.factor(zoo1$tail)
zoo1$domestic <- as.factor(zoo1$domestic)
zoo1$catsize <- as.factor(zoo1$catsize)
zoo1$type <- as.factor(zoo1$type)
# Data partition
set.seed(123)
ind <- sample(2,nrow(zoo1), replace = T, prob = c(0.7,0.3))
train <- zoo1[ind==1,]
test <- zoo1[ind==2,]
# KNN Model
trcontrol <- trainControl(method = "repeatedcv", number = 10,repeats = 3
# classprobs are needed when u want to select ROC for optimal K Value
)
set.seed(222)
fit <- train(type ~., data = train, method = 'knn', tuneLength = 20,
trControl = trcontrol, preProc = c("center","scale"))
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs5
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
## Warning in preProcess.default(thresh = 0.95, k = 5, freqCut = 19, uniqueCut
## = 10, : These variables have zero variances: legs8
# default metric is accuracy but if u want to use ROC, then mention the same
# Model Performance :
fit # the optimum value for k should be 7
## k-Nearest Neighbors
##
## 72 samples
## 16 predictors
## 7 classes: '1', '2', '3', '4', '5', '6', '7'
##
## Pre-processing: centered (20), scaled (20)
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 65, 63, 66, 64, 65, 65, ...
## Resampling results across tuning parameters:
##
## k Accuracy Kappa
## 5 0.9040741 0.8751166
## 7 0.9062963 0.8773174
## 9 0.8635847 0.8214341
## 11 0.8599339 0.8144032
## 13 0.8464815 0.7974134
## 15 0.7956878 0.7305769
## 17 0.7781349 0.7089185
## 19 0.7551852 0.6774064
## 21 0.7340873 0.6471112
## 23 0.7158995 0.6229688
## 25 0.7164947 0.6235111
## 27 0.7069709 0.6072264
## 29 0.6680556 0.5490114
## 31 0.6216931 0.4701934
## 33 0.5720899 0.3900492
## 35 0.5485847 0.3462768
## 37 0.5234524 0.3002657
## 39 0.4913492 0.2441984
## 41 0.4620503 0.1877586
## 43 0.4303042 0.1312393
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was k = 7.
plot(fit)
varImp(fit)
## ROC curve variable importance
##
## variables are sorted by maximum importance across the classes
## X1 X2 X3 X4 X5 X6 X7
## milk 100.000 100.00 100.000 100.000 100.000 100.000 100.000
## breathes 20.000 100.00 0.000 0.000 75.000 20.000 100.000
## feathers 100.000 100.00 100.000 100.000 100.000 100.000 0.000
## legs 70.370 96.30 70.370 100.000 70.370 70.370 96.296
## backbone 0.000 0.00 0.000 100.000 100.000 0.000 0.000
## hair 100.000 100.00 100.000 100.000 100.000 100.000 100.000
## eggs 96.296 96.30 96.296 96.296 96.296 96.296 96.296
## toothed 96.296 96.30 96.296 96.296 96.296 96.296 16.296
## fins 7.407 92.59 7.407 7.407 7.407 7.407 92.593
## aquatic 8.262 85.19 85.185 14.815 47.685 8.262 85.185
## tail 18.519 18.52 81.481 81.481 81.481 18.519 18.519
## airborne 69.516 69.52 69.516 75.926 69.516 76.923 7.407
## catsize 54.074 51.00 74.074 74.074 61.574 50.997 54.074
## venomous 40.000 0.00 50.000 33.333 12.500 40.000 40.000
## predator 31.852 24.58 5.698 31.481 26.852 26.154 31.852
## domestic 18.519 18.52 18.519 10.826 18.519 10.826 18.519
pred <- predict(fit, newdata = test )
confusionMatrix(pred, test$type)
## Confusion Matrix and Statistics
##
## Reference
## Prediction 1 2 3 4 5 6 7
## 1 12 0 0 0 0 0 0
## 2 0 7 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 2 0 0 2 2 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 2 1
## 7 0 0 0 0 0 0 1
##
## Overall Statistics
##
## Accuracy : 0.8276
## 95% CI : (0.6423, 0.9415)
## No Information Rate : 0.4828
## P-Value [Acc > NIR] : 0.0001373
##
## Kappa : 0.7599
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: 1 Class: 2 Class: 3 Class: 4 Class: 5 Class: 6
## Sensitivity 0.8571 1.0000 NA 1.00000 0.00000 1.00000
## Specificity 1.0000 1.0000 1 0.85185 1.00000 0.96296
## Pos Pred Value 1.0000 1.0000 NA 0.33333 NaN 0.66667
## Neg Pred Value 0.8824 1.0000 NA 1.00000 0.93103 1.00000
## Prevalence 0.4828 0.2414 0 0.06897 0.06897 0.06897
## Detection Rate 0.4138 0.2414 0 0.06897 0.00000 0.06897
## Detection Prevalence 0.4138 0.2414 0 0.20690 0.00000 0.10345
## Balanced Accuracy 0.9286 1.0000 NA 0.92593 0.50000 0.98148
## Class: 7
## Sensitivity 0.50000
## Specificity 1.00000
## Pos Pred Value 1.00000
## Neg Pred Value 0.96429
## Prevalence 0.06897
## Detection Rate 0.03448
## Detection Prevalence 0.03448
## Balanced Accuracy 0.75000
# 89.66 % is accuracy