R confusionMatrix() & Table() дает ошибку в sort.list (y): 'x' должен быть атомарным для 'sort.list' Вы вызвали сортировку в списке?

Question

Я пытаюсь создать матрицу путаницы, предпочтительно используя функцию confusionMatrix(), однако я получаю эту ошибку: Ошибка в sort.list (y): 'x' должен быть атомарным для 'sort.list' Вы назвали «сортировку» в списке?

Я также попытался использовать функцию table(), но получил ту же ошибку.

Ниже весь мой код:

#install load libraries 
install.packages('MASS') 
install.packages('tree') 
install.packages("e1071") 
install.packages("caret") 
library('MASS') 
library('tree') 
library('e1071') 
library('caret') 
set.seed(1985) 

#GET DATA 
training <- read.csv("C:/Users/anaim/data_minig_project/pml-training.csv",header=TRUE, sep=",", na.strings="NA", dec=".", strip.white=TRUE) 
training_df <- data.frame(training,stringsAsFactors=FALSE) 
nrow(training_df) 
ncol(training_df) 

#create train & test set splits 
inTrain <- createDataPartition(y=training_df$classe, p=0.75, list=FALSE) 
training_data <- training_df[inTrain,] 
testing_data<- training_df[-inTrain,] 

#FEATURE SELECTION & DATA CLEANING 
#one can see numbers of features is quite large with 160 columns, therefore we will refer to the studies such as paper #1 to start and reduce the number of features 

#subset based on features mentioned studies 
training_data_subset <- subset(training_data, select=c("avg_roll_belt","var_roll_belt","var_total_accel_belt","amplitude_roll_belt","max_roll_belt","var_roll_belt", 
                 "var_accel_arm","magnet_arm_x","magnet_arm_y","magnet_arm_z","accel_dumbbell_y","accel_dumbbell_z","magnet_dumbbell_x","gyros_dumbbell_x", 
                 "gyros_dumbbell_y","gyros_dumbbell_z","pitch_forearm","gyros_forearm_x","gyros_forearm_y","classe")) 

#subset based on features mentioned studies 
testing_data_subset <- subset(testing_data, select=c("avg_roll_belt","var_roll_belt","var_total_accel_belt","amplitude_roll_belt","max_roll_belt","var_roll_belt", 
                "var_accel_arm","magnet_arm_x","magnet_arm_y","magnet_arm_z","accel_dumbbell_y","accel_dumbbell_z","magnet_dumbbell_x","gyros_dumbbell_x", 
                "gyros_dumbbell_y","gyros_dumbbell_z","pitch_forearm","gyros_forearm_x","gyros_forearm_y","classe")) 


#all NAs to 0 
testing_data_subset[is.na(testing_data_subset)] <- 0 
training_data_subset[is.na(training_data_subset)] <- 0 

#load library(e1071) before using skewness() 
#load library(e1071) befortraining_datae using skewness() 
#investigate skewness 
# Interpretation of skewness - http://www.tc3.edu/instruct/sbrown/stat/shape.htm#SkewnessCompute 

skewness_result <- apply(training_data_subset[, sapply(training_data_subset, is.numeric)], 2, skewness) 

skewness_df <- data.frame(skewness_result) 

#remove highly skewed columns 
remove <- c("var_roll_belt","var_total_accel_belt","amplitude_roll_belt","var_roll_belt","var_roll_belt.1","magnet_dumbbell_x") 
training_data_subset <- training_data_subset[, !(colnames(training_data_subset) %in% remove), drop=FALSE] 
testing_data_subset <- testing_data_subset[, !(colnames(testing_data_subset) %in% remove), drop=FALSE] 

#valid columns were removed 
ncol(training_data_subset) 
ncol(testing_data_subset) 

#BUILD MODEL 

#1)decision tree 

exercise.model <- tree(formula = classe ~ ., data = training_data_subset) 
summary(exercise.model) 

plot(exercise.model) 
text(exercise.model ,pretty =0) 

#MODEL EVALUATION 
exercise.prediction <- predict(exercise.model,newdata = testing_data_subset, type="tree") 

**#THIS IS WERE I GET THE ERROR** 
confusionMatrix(exercise.prediction,testing_data_subset[['classe']]) 
confusionMatrix(exercise.prediction,testing_data_subset$classe) 

**# I also tried table() just to get raw True (positive + True Negatives/Total) values but I got the same error** 

table(exercise.prediction, testing_data_subset[['classe']]) 
table(exercise.prediction,testing_data_subset$classe) 

Любая помощь в создании матрицы неточностей с помощью confusionMatrix() будет оценен.

Благодаря

Answer 1

дерева() функцией является базовой функцией R для построения дерева решений, однако confusionMatrix() является частью пакета CARET, поэтому выход из дерева() был по какой-то причине, не совместимой с confusionMatrix(). Когда я заменил tree() на exercise.model <- train(classe ~ ., preProcess = c("center", "scale", "BoxCox", "pca"), data =training_data_subset ,method ="rpart"); Чем я наработал функции confusionMatrix() и table().