metrics. f1_score

f1_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None)

Compute the F1 score, also known as balanced F-score or F-measure

The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The relative contribution of precision and recall to the F1 score are equal. The formula for the F1 score is::

F1 = 2 * (precision * recall) / (precision + recall)

Parameters:	y_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order. pos_label : str or int, 1 by default The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only. average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', 'weighted'] This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. sample_weight : array-like of shape = [n_samples], optional Sample weights.
Returns:	f1_score : float or array of float, shape = [n_unique_labels]

Parameters:

y_true : 1d array-like, or label indicator array / sparse matrix: Ground truth (correct) target values.
y_pred : 1d array-like, or label indicator array / sparse matrix: Estimated targets as returned by a classifier.
labels : list, optional: The set of labels to include when ``average != 'binary'``, and their order if ``average is None``. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in ``y_true`` and ``y_pred`` are used in sorted order.
pos_label : str or int, 1 by default: The class to report if ``average='binary'`` and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting ``labels=[pos_label]`` and ``average != 'binary'`` will report scores for that label only.
average : string, [None, 'binary' (default), 'micro', 'macro', 'samples', 'weighted']: This parameter is required for multiclass/multilabel targets. If ``None``, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data.
sample_weight : array-like of shape = [n_samples], optional: Sample weights.

Returns:

f1_score : float or array of float, shape = [n_unique_labels]