imblearn.metrics.classification_report_imbalanced

imblearn.metrics.classification_report_imbalanced(y_true, y_pred, labels=None, target_names=None, sample_weight=None, digits=2, alpha=0.1)[source][source]

Build a classification report based on metrics used with imbalanced dataset

Specific metrics have been proposed to evaluate the classification performed on imbalanced dataset. This report compiles the state-of-the-art metrics: precision/recall/specificity, geometric mean, and index balanced accuracy of the geometric mean.

Parameters:
y_true : ndarray, shape (n_samples, )

Ground truth (correct) target values.

y_pred : ndarray, shape (n_samples, )

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.

target_names : list of strings, optional

Optional display names matching the labels (same order).

sample_weight : ndarray, shape (n_samples, )

Sample weights.

digits : int, optional (default=2)

Number of digits for formatting output floating point values

alpha : float, optional (default=0.1)

Weighting factor.

Returns:
report : string

Text summary of the precision, recall, specificity, geometric mean, and index balanced accuracy.

Examples

>>> import numpy as np
>>> from imblearn.metrics import classification_report_imbalanced
>>> y_true = [0, 1, 2, 2, 2]
>>> y_pred = [0, 0, 2, 2, 1] # doctest : +NORMALIZE_WHITESPACE
>>> target_names = ['class 0', 'class 1',     'class 2'] # doctest : +NORMALIZE_WHITESPACE
>>> print(classification_report_imbalanced(y_true, y_pred,     target_names=target_names))
                   pre       rec       spe        f1       geo       iba       sup
<BLANKLINE>
    class 0       0.50      1.00      0.75      0.67      0.87      0.77         1
    class 1       0.00      0.00      0.75      0.00      0.00      0.00         1
    class 2       1.00      0.67      1.00      0.80      0.82      0.64         3
<BLANKLINE>
avg / total       0.70      0.60      0.90      0.61      0.66      0.54         5
<BLANKLINE>