Error in ConfusionMatrix the data and reference factors must have the same number of levels

Understanding the Error

The error “Error in ConfusionMatrix: the data and reference factors must have the same number of levels” arises in R when using the confusionMatrix function from the caret package. This indicates a mismatch in the number of unique categories (levels) present in the predicted and actual (reference) outcomes.

Example Scenario

Imagine predicting whether a customer will click on an ad (Click or No Click). The actual outcome (reference) has two levels (“Click”, “No Click”). However, the prediction model outputs three levels (“Click”, “No Click”, “Maybe Click”). This discrepancy will trigger the error.

Causes of the Error

• Unequal Levels: The most common cause is simply having a different number of categories in the predicted and actual data.
• Typographical Errors: Misspellings or inconsistent capitalization in levels can lead to R treating them as separate categories.
• Data Transformation: Certain data transformations might unintentionally alter the number of levels in your data.

Troubleshooting Steps

1. Inspect Your Data

• Use unique() to view the unique levels in your predicted and actual data columns.
• Ensure the levels are spelled identically and have consistent capitalization.
• Verify that the levels in your reference data are all present in your predicted data, and vice versa.

2. Rectify Levels

• Merge Levels: If you have extraneous levels in your prediction, consider merging them into existing categories (e.g., combining “Maybe Click” with “Click”).
• Re-assign Levels: If you have missing levels in your prediction, carefully re-assign values to match your reference data.

3. Re-run the Analysis

After making the necessary adjustments, re-run your model and the confusionMatrix function. You should now receive the desired confusion matrix output.

Illustrative Example

Sample Code (Before Correction)

library(caret)

# Example Data (Note discrepancy in levels)
actual <- c("Click", "No Click", "Click", "No Click", "Click")
predicted <- c("Click", "No Click", "Click", "No Click", "Maybe Click")

# Attempting to create a confusion matrix
confusionMatrix(predicted, actual)

Output

Error in ConfusionMatrix: the data and reference factors must have the same number of levels 

Sample Code (After Correction)

# Correcting the levels
predicted <- ifelse(predicted == "Maybe Click", "Click", predicted)

# Creating the confusion matrix
confusionMatrix(predicted, actual)

Corrected Output

Confusion Matrix and Statistics

          Reference
Prediction Click No Click
   Click     3       0
 No Click    0       2

               Accuracy : 1          
                 95% CI : (0.5965, 1)
    No Information Rate : 0.6        
    P-Value [Acc > NIR] : 0.0119      
                  Kappa : 1          
 Mcnemar's Test P-Value : NA         

Sensitivity : 1          
Specificity : 1          
 Pos Pred Value : 1          
 Neg Pred Value : 1          
             Prevalence : 0.6        
         Detection Rate : 0.6        
   Detection Prevalence : 0.6        
      Balanced Accuracy : 1          

       'Positive' Class : Click 

Conclusion

The "Error in ConfusionMatrix" regarding differing levels is a common issue that can be resolved with careful data inspection and appropriate adjustments. By ensuring your predicted and actual outcomes have the same categories and accurately reflect the data, you can successfully generate and interpret your confusion matrix for effective model evaluation.