How to solve Error in Confusion Matrix

In this article, we will discuss what is Confusion Matrix and what are the causes of the error in a Confusion Matrix and How to solve an Error in a Confusion Matrix in R Programming Language.

What is Confusion Matrix?

A confusion matrix is an essential tool for assessing a model’s performance in machine learning and classification tasks. It offers a thorough explanation of how the model’s predictions compare to the actual ground truth values. Improving the effectiveness of classification algorithms requires an understanding of the subtleties of a confusion matrix.

There are two sorts of mistakes that can arise in a confusion matrix

1. Type I Error (False Positive): This occurs when the model predicts a positive outcome but the actual outcome is negative.
2. Type II Error (False Negative): This occurs when the model predicts a negative result when the true outcome is good. Distinguishing between these faults is critical since they have diverse repercussions in various applications.

Common Errors in Confusion Matrix

There are two types of error occur most of the time

1. Incorrect Input Format
2. Incorrect Function Syntax

Incorrect Input Format

This errors arises when the input vectors for actual and expected values mismatch in length or format.

R

# Creating a confusion matrix with incorrect input format
actual <- c(1, 0, 1, 0, 1)
predicted <- c(0, 0, 1, 1)  # Incorrect length of 'predicted'
conf_matrix <- table(actual, predicted)

Output:

Error in table(actual, predicted) : 
  all arguments must have the same length
Execution halted

To handle this error ensure that both the actual and predicted vectors have the same length to create a valid confusion matrix.

R

# Creating a confusion matrix with correct input format
actual <- c(1, 0, 1, 0, 1)
predicted <- c(0, 0, 1, 1, 1) 

# Handling Incorrect Input Format
# Check and correct the length of 'predicted'
if (length(actual) != length(predicted)) {
  stop("Length of 'actual' and 'predicted' vectors must be equal.")
} else {
  # Create confusion matrix
  conf_matrix <- table(actual, predicted)
}
conf_matrix

Output:

      predicted
actual 0 1
     0 1 1
     1 1 2

Incorrect Function Syntax

This error arises when an improper function name or syntax is used to generate the confusion matrix. In R, the confMatrix() function does not exist by default, therefore attempting to use it will result in an error.

R

# Creating a confusion matrix with inconsistent class labels
actual <- c(1, 0, 1, 0, 1)
predicted <- c("positive", "negative", "positive", "positive", "positive")
conf_matrix <-confMatrix(actual, predicted)
conf_matrix

Output:

Error in confMatrix(actual, predicted) : 
  could not find function "confMatrix"
Execution halted

To handle this error ensure that you use the correct function to create the confusion matrix. In R, the appropriate function for creating a confusion matrix is table().

R

actual <- c(1, 0, 1, 0, 1)
predicted <- c("positive", "negative", "positive", "positive", "positive")
conf_matrix <- table(actual, predicted)
conf_matrix 

Output:

     predicted
actual negative positive
     0        1        1
     1        0        3

Conclusion

In conclusion, understanding confusion matrices in R is critical for accurately evaluating classification models. Users may assure accurate insights by resolving frequent problems and using appropriate troubleshooting strategies. Using confusion matrices improves data analysis workflows for optimal outcomes by paying attention to detail and following best practices.