Precision, Recall and F1-Score using R

In machine learning, evaluating model performance is critical. Three widely used metrics—Precision, Recall, and F1-Score—help assess the quality of classification models. Here's what each metric represents:

1. Recall: Measures the proportion of actual positive cases correctly identified. Also known as sensitivity or true positive rate (TPR).
   Formula:
2. Precision: Represents the proportion of positive predictions that are correct.
   Formula:
3. F1-Score: The harmonic mean of Precision and Recall, offering a balance between the two.
   Formula:

Calculating Precision, Recall and F1 Score in R

R provides robust tools to compute these metrics easily. Below are step-by-step instructions using caret and Metrics packages.

Step 1: Install and Load Required Packages

To compute Recall and related metrics, first install the necessary R packages.

Load the packages:

Step 2: Compute Recall Using the caret Package

The caret::confusionMatrix() function computes a confusion matrix along with Recall and other metrics. Here's an example:

Output

Confusion Matrix and Statistics

 actual
predicted 0 1
 0 0 2
 1 1 2

 Accuracy : 0.4 
 95% CI : (0.0527, 0.8534)
 No Information Rate : 0.8 
 P-Value [Acc > NIR] : 0.9933 

 Kappa : -0.3636 

 Mcnemar's Test P-Value : 1.0000 

 Sensitivity : 0.0000 
 Specificity : 0.5000 
 Pos Pred Value : 0.0000 
 Neg Pred Value : 0.6667 
 Prevalence : 0.2000 
 Detection Rate : 0.0000 
 Detection Prevalence : 0.4000 
 Balanced Accuracy : 0.2500 

 'Positive' Class : 0 

Step 3: Compute Metrics Using the Metrics Package

The Metrics package offers functions like recall(), precision(), and f1_score() for quick calculation.

Output

Recall: 0.5 
Precision: 0.6666667 
F1-Score: 1 

• Recall is for applications like disease detection or fraud detection where missing positive cases is costly.
• Precision is for spam filters or recommendation systems where false positives should be minimized.
• F1-Score balances precision and recall, making it ideal for imbalanced datasets.

R programming makes it simple to compute metrics like Precision, Recall, and F1-Score using packages like caret and Metrics. These metrics are indispensable for evaluating and improving model performance, particularly for binary classification problems.