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Confusion matrix with more than 500 possible categorical outcomes in R

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0

I am running naive Bayes classifiers as my all predictors and outcome variables are categorical. Trying to predict which client (around 700 different client names) will buy a specific product. The products have categorical specifications – country origin, color, size, is it in promotion, etc. When I try to see the accuracy with confusion matrix, the results are so messy and long that I could make sense of it. Does anyone have an idea how to visualize confusion matrix with more than 500 possible categorical outcomes? Or maybe there is other way to visualize differently the results?

library(e1071)
library(caret)
library(naivebayes)

data <- read.csv("Data.csv")

set.seed(2)
random <- sample(2, nrow(data1), prob = c(0.7, 0.3), replace = T)
data_train <- data[random == 1, ]
data_test <- data[random == 2, ]
data_nb <- naiveBayes(Client.Name ~., data = data_train)
pred_nb <- predict(data_nb, data_test)
confusionMatrix(table(pred_nb, data_test$Client.Name))

r confusion-matrix

share|improve this question

edited Mar 24 at 18:37

Rosi Ilieva

asked Mar 24 at 17:16

Rosi IlievaRosi Ilieva

112

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Can you either give a sample of confusion matrix or the data to get it?
  
  – Oka
  Mar 24 at 17:31
  

  
  
  
  

add a comment | 

0

I am running naive Bayes classifiers as my all predictors and outcome variables are categorical. Trying to predict which client (around 700 different client names) will buy a specific product. The products have categorical specifications – country origin, color, size, is it in promotion, etc. When I try to see the accuracy with confusion matrix, the results are so messy and long that I could make sense of it. Does anyone have an idea how to visualize confusion matrix with more than 500 possible categorical outcomes? Or maybe there is other way to visualize differently the results?

library(e1071)
library(caret)
library(naivebayes)

data <- read.csv("Data.csv")

set.seed(2)
random <- sample(2, nrow(data1), prob = c(0.7, 0.3), replace = T)
data_train <- data[random == 1, ]
data_test <- data[random == 2, ]
data_nb <- naiveBayes(Client.Name ~., data = data_train)
pred_nb <- predict(data_nb, data_test)
confusionMatrix(table(pred_nb, data_test$Client.Name))

r confusion-matrix

share|improve this question

edited Mar 24 at 18:37

Rosi Ilieva

asked Mar 24 at 17:16

Rosi IlievaRosi Ilieva

112

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Can you either give a sample of confusion matrix or the data to get it?
  
  – Oka
  Mar 24 at 17:31
  

  
  
  
  

add a comment | 

I am running naive Bayes classifiers as my all predictors and outcome variables are categorical. Trying to predict which client (around 700 different client names) will buy a specific product. The products have categorical specifications – country origin, color, size, is it in promotion, etc. When I try to see the accuracy with confusion matrix, the results are so messy and long that I could make sense of it. Does anyone have an idea how to visualize confusion matrix with more than 500 possible categorical outcomes? Or maybe there is other way to visualize differently the results?

library(e1071)
library(caret)
library(naivebayes)

data <- read.csv("Data.csv")

set.seed(2)
random <- sample(2, nrow(data1), prob = c(0.7, 0.3), replace = T)
data_train <- data[random == 1, ]
data_test <- data[random == 2, ]
data_nb <- naiveBayes(Client.Name ~., data = data_train)
pred_nb <- predict(data_nb, data_test)
confusionMatrix(table(pred_nb, data_test$Client.Name))

r confusion-matrix

share|improve this question

edited Mar 24 at 18:37

Rosi Ilieva

asked Mar 24 at 17:16

Rosi IlievaRosi Ilieva

112

library(e1071)
library(caret)
library(naivebayes)

data <- read.csv("Data.csv")

set.seed(2)
random <- sample(2, nrow(data1), prob = c(0.7, 0.3), replace = T)
data_train <- data[random == 1, ]
data_test <- data[random == 2, ]
data_nb <- naiveBayes(Client.Name ~., data = data_train)
pred_nb <- predict(data_nb, data_test)
confusionMatrix(table(pred_nb, data_test$Client.Name))

share|improve this question

edited Mar 24 at 18:37

Rosi Ilieva

asked Mar 24 at 17:16

Rosi IlievaRosi Ilieva

112

share|improve this question

edited Mar 24 at 18:37

Rosi Ilieva

asked Mar 24 at 17:16

Rosi IlievaRosi Ilieva

112

asked Mar 24 at 17:16

Rosi IlievaRosi Ilieva

112

asked Mar 24 at 17:16

Rosi IlievaRosi Ilieva

112

1 Answer
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Assuming your matrix have numerical values for accuracy, you can conveniently visualize it as a heatmap. As accuracy is between 0 and 100%, you don´t even need to normalize it. You can use ggplot2 or heatmap.2 package for that purpose.

share|improve this answer

answered Mar 24 at 18:03

OkaOka

76829

add a comment | 

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1

Assuming your matrix have numerical values for accuracy, you can conveniently visualize it as a heatmap. As accuracy is between 0 and 100%, you don´t even need to normalize it. You can use ggplot2 or heatmap.2 package for that purpose.

share|improve this answer

answered Mar 24 at 18:03

OkaOka

76829

add a comment | 

1

Assuming your matrix have numerical values for accuracy, you can conveniently visualize it as a heatmap. As accuracy is between 0 and 100%, you don´t even need to normalize it. You can use ggplot2 or heatmap.2 package for that purpose.

share|improve this answer

answered Mar 24 at 18:03

OkaOka

76829

add a comment | 

Assuming your matrix have numerical values for accuracy, you can conveniently visualize it as a heatmap. As accuracy is between 0 and 100%, you don´t even need to normalize it. You can use ggplot2 or heatmap.2 package for that purpose.

share|improve this answer

answered Mar 24 at 18:03

OkaOka

76829

share|improve this answer

answered Mar 24 at 18:03

OkaOka

76829

answered Mar 24 at 18:03

OkaOka

76829

answered Mar 24 at 18:03

OkaOka

76829