Understanding the Error: Unused Argument When Building a Confusion Matrix in R

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In this article, we will explore the error that occurs when building a confusion matrix using the confusionMatrix function in R. We will delve into the code, identify the source of the issue, and provide a step-by-step solution to resolve it.

Introduction

A confusion matrix is a table used to summarize the performance of a classification model by displaying true positives, false positives, true negatives, and false negatives. In this article, we will focus on building a confusion matrix using the confusionMatrix function from the caret package in R.

The Error

The error message displayed when running the following code snippet:

    #### Logistic regression model
    log_model = glm(Satisfaction~., data = lgtrain, family = "binomial")
    summary(log_model)
    log_preds = predict(log_model, lgtest[,1:22], type = "response")
    head(log_preds)
    log_class = array(c(99))
    for (i in 1:length(log_preds)){
      if(log_preds[i]&gt;0.5){
        log_class[i]="satisfied"}else{log_class[i]="neutral or dissatisfied"}}
    
    ### Creating a new modelframe containing the actual and predicted values.
    log_result = data.frame(Actual = lgtest$Satisfaction, Prediction = log_class)
    lgtest$Satisfaction = factor(lgtest$Satisfaction, c(1,0),labels=c("satisfied","neutral or dissatisfied"))
    confusionMatrix(log_class, log_preds, threshold = 0.5) ####this works 

    mr1 = confusionMatrix(as.factor(log_class),lgtest$Satisfaction, positive = "satisfied") ## this is the line that causes the error
    
</code>

The error message displayed when running the code snippet above is:

Error in confusionMatrix(as.factor(log_class), lgtest$Satisfaction, positive = "satisfied") : 
  unused argument (positive = "satisfied")

Understanding the `confusionMatrix` Function

The confusionMatrix function from the caret package is used to create a confusion matrix. The function requires three arguments:

as.factor(x): This is the predicted class or label, which should be converted into a factor.
y: This is the actual class or label.
[["positive", "negative"] = "value"]`: This specifies the positive and negative classes.

In the code snippet above, we are using as.factor(log_class) as the predicted class, lgtest$Satisfaction as the actual class, and "satisfied" as the positive class.

Resolving the Error

The error occurs because the positive argument is not required when using the confusionMatrix function with a factor. The default value for positive is TRUE.

To resolve the error, we can modify the code to remove the positive argument:

#### Logistic regression model
log_model = glm(Satisfaction~., data = lgtrain, family = "binomial")
summary(log_model)
log_preds = predict(log_model, lgtest[,1:22], type = "response")
head(log_preds)
log_class = array(c(99))
for (i in 1:length(log_preds)){
 if(log_preds[i]&gt;0.5){
   log_class[i]="satisfied"}else{log_class[i]="neutral or dissatisfied"}}
### Creating a new modelframe containing the actual and predicted values.
log_result = data.frame(Actual = lgtest$Satisfaction, Prediction = log_class)
lgtest$Satisfaction = factor(lgtest$Satisfaction, c(1,0),labels=c("satisfied","neutral or dissatisfied"))
confusionMatrix(log_class, log_preds, threshold = 0.5) ####this works 

mr1 = confusionMatrix(as.factor(log_class), lgtest$Satisfaction)

By removing the positive argument, we can resolve the error and run the code without any issues.

Additional Considerations

When building a confusion matrix using the confusionMatrix function, it’s essential to understand the different parameters and their meanings:

x: The predicted class or label.
y: The actual class or label.
[["positive", "negative"] = "value"]`: Specifies the positive and negative classes.

By understanding these parameters and how they are used, you can effectively build a confusion matrix using the confusionMatrix function in R.

Best Practices

Here are some best practices to keep in mind when building a confusion matrix:

Always specify the correct arguments for the confusionMatrix function.
Understand the different parameters and their meanings.
Use the correct units for the predicted class or label (e.g., 0 or 1).
Consider using the threshold argument to adjust the sensitivity of the confusion matrix.

By following these best practices, you can build an accurate and informative confusion matrix that helps evaluate the performance of your classification model.

Last modified on 2024-06-27