get_recall

get_recall(y_true, y_pred)

Calculates the recall of predictions and returns the result. Recall is the proportion of actual positive cases that were correctly identified. It answers: “Of all the items that were actually positive, how many did we catch?” Recall = True Positives / (True Positives + False Negatives). Scoring is between 0 and 1 with a perfect recall being 1.

Parameters

Name	Type	Description	Default
y_true	array	The actual observed values (ground truth).	required
y_pred	array	The model predicted values.	required

Returns

Name	Type	Description
	float	The calculated recall score, ranging from 0.0 to 1.0.

Examples

>>> # Perfect recall
>>> y_true = [0, 1, 1, 0]
>>> y_pred = [0, 1, 1, 0]
>>> get_recall(y_true, y_pred)
1.0
>>> # Partial recall
>>> y_true = [0, 1, 1, 0]
>>> y_pred = [0, 1, 0, 0]
>>> get_recall(y_true, y_pred)
0.5