Understanding Confusion Matrices and the Error Message

Confusion matrices are a fundamental tool in evaluating the performance of classification models. They provide a summary of the predictions made by the model, comparing them to the actual outcomes. However, when working with confusion matrices, it’s essential to understand the structure and requirements of the data used to generate them.

In this article, we’ll delve into the error message encountered while creating a confusion matrix using R and the SVM algorithm. We’ll explore the cause of the issue, potential solutions, and provide guidance on how to resolve this common problem.

The Error Message

The error message “Error in confusionMatrix.default(test.pred, data_test$FAVOURITES_COUNT) : the data cannot have more levels than the reference” suggests that there’s an issue with the structure of the data used to create the confusion matrix. Specifically, it implies that the data has more levels (i.e., categories or classes) than the reference values.

Understanding Data Levels and Reference Values

In the context of confusion matrices, “levels” refer to the unique categories or classes present in the data. The “reference” values are typically the labels or outcomes assigned to these categories.

For example, suppose we’re working with a binary classification problem, where our data has two levels: 0 (negative class) and 1 (positive class). In this case, the reference values would be “0” and “1”.

The Issue at Hand

In the provided R code, we have:

# Predictions made by the model
test.pred <- predict(model, data_test, na.action = na.pass)

Here, test.pred is a vector containing the predicted outcomes. The issue arises when trying to create a confusion matrix using this vector and the reference values from data_test$FAVOURITES_COUNT.

The error message indicates that the data has more levels (categories) than the reference values. In other words, data_test$FAVOURITES_COUNT contains multiple categories or classes, while the predicted outcomes (test.pred) are limited to a single outcome.

Why Does This Happen?

There are several reasons why this issue might occur:

• The algorithm is predicting results only for one outcome.
• The test set has an imbalance between the classes (e.g., more of one class than the other).
• There’s a missing or inconsistent value in the data that affects the classification.

Solutions and Workarounds

To resolve this error, we need to ensure that our test set has enough instances of both classes. Here are some potential solutions:

1. Increase the Number of Instances per Class

If the issue arises from an imbalance between the classes, we can try increasing the number of instances for each class in the test set.

# Increase the number of instances per class
set.seed(123)
trainIndex <- createDataPartition(mydata$FAVOURITES_COUNT, p=0.5, list=FALSE)
data_train <- mydata[trainIndex,]
data_test <- mydata[-trainIndex,]

model <- svm(FAVOURITES_COUNT~., data = data_train);

In this example, we increase the proportion of instances for each class in the test set.

2. One-vs-All (OvA) Approach

If the algorithm is predicting results only for one outcome, we can use the OvA approach to create a confusion matrix that includes all classes.

# One-vs-All (OvA) approach
library(rpart)
model <- rpart(FAVOURITES_COUNT ~ ., data = mydata)
test.pred <- predict(model, newdata = data_test)

confusionMatrix(test.pred, data_test$FAVOURITES_COUNT)

In this example, we use the rpart package to create a decision tree model and then make predictions using the OvA approach.

3. Handling Missing or Inconsistent Values

If there’s a missing or inconsistent value in the data that affects the classification, we can try handling these values before creating the confusion matrix.

# Handle missing or inconsistent values
data_test$FAVOURITES_COUNT[is.na(data_test$FAVOURITES_COUNT)] <- 0

model <- svm(FAVOURITES_COUNT~., data = data_train);
test.pred <- predict(model, data_test)

In this example, we replace missing or inconsistent values with a default value (in this case, 0).

Conclusion

Creating confusion matrices is an essential step in evaluating the performance of classification models. However, when working with these matrices, it’s crucial to understand the structure and requirements of the data used to generate them.

By understanding the issue at hand and applying potential solutions, we can resolve common problems encountered while creating confusion matrices.

Last modified on 2023-06-06