Recall

• Recall Score is a metric used to evaluate the performance of a Classification model, specifically focusing on the model’s ability to identify all relevant instances of the positive class.
• It is defined as the proportion of true positive predictions among all actual positive instances.
• Classification Report & Precision or Recall
• The formula for recall is: $\text{Recall}=\frac{TP}{TP+FN}$ where:
  • TP (True Positives): The number of correctly predicted positive instances.
  • FN (False Negatives): The number of actual positive instances that were incorrectly predicted as negative.

Importance

• Recall is crucial in scenarios where missing a positive instance is costly, such as in disease screening or fraud detection.
• It helps in understanding how well the model captures all the actual positive instances.

Implementing Recall Score in Python

You can calculate the recall score in Python using the scikit-learn library, which provides a convenient function for this purpose.

Example Usage

from sklearn.metrics import recall_score
 
# Sample true labels and predicted labels
y_true = [0, 1, 1, 0, 1, 0, 1, 1]
y_pred = [0, 1, 0, 0, 1, 1, 1, 1]
 
# Calculate recall score
recall = recall_score(y_true, y_pred)
 
print("Recall Score: {:.2f}".format(recall))

• The recall_score function can handle binary and multiclass classification problems. For multiclass, you can specify the average parameter to determine how to calculate the recall (e.g., average='macro', average='micro', or average='weighted').