get_f1

get_f1(y_true, y_pred)

Calculates the F1 score of predictions and returns the result. The F1 score is the harmonic mean of precision and recall. It provides a balanced measure between precision and recall, useful when you want to balance the trade-off between false positives and false negatives. F1 = 2 * ((Precision * Recall) / (Precision + Recall)). Scoring is between 0 and 1 with a perfect F1 score 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 F1 score, ranging from 0.0 to 1.0.

Examples

>>> # Perfect F1 score
>>> y_true = [0, 1, 1, 0]
>>> y_pred = [0, 1, 1, 0]
>>> get_f1(y_true, y_pred)
1.0
>>> # Partial F1 score
>>> y_true = [0, 1, 1, 0]
>>> y_pred = [0, 1, 0, 0]
>>> get_f1(y_true, y_pred)
0.6666666666666666