Evaluating Model Prediction Performance with Recall
In machine learning, when evaluating the performance of a classification model, Recall is as important an indicator as precision.
Recall indicates how well a model predicts actual positive data without missing any.
It is calculated using the following formula:
Here, each term means:
-
TP (True Positive): Instances where the model correctly identifies a positive condition -
FN (False Negative): Instances where the model incorrectly predicts a negative condition on actual positives
In simple terms, recall shows how accurately a machine learning model identifies truly positive situations.
For example, in a cancer diagnosis model, high recall means the model correctly identifies most patients who truly have cancer.
When Recall is Important
Recall is especially important in the following situations:
1. Disease Diagnosis Systems (Cancer Diagnosis Model)
Failing to detect a cancer patient can have serious consequences.
In such cases, recall is often more crucial than precision.
Low recall increases the risk of incorrectly predicting cancer patients as healthy individuals.
2. Disaster Alert Systems
In systems such as fire alarms or earthquake alerts, even a single missed alert could be catastrophic.
Therefore, it's important to prioritize recall even if it leads to false positives.
Limitations of Recall
Having a model with high recall doesn't always mean it's the best.
Extremely high recall can come at the cost of very low precision.
For instance, in a financial fraud detection model, increasing recall by broadly categorizing transactions as suspicious could result in mistakenly identifying legitimate transactions as fraudulent.
That’s why model evaluation should consider both precision and recall.
To balance the trade-off between the two, a comprehensive evaluation metric called the F1-Score is often used.
In the next session, we will explore the F1-Score.
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