sklearn.metrics.matthews_corrcoef

sklearn.metrics.matthews_corrcoef(y_true, y_pred, sample_weight=None)

Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a measure of the quality of binary (two-class) classifications. It takes into account true and false positives and negatives and is generally regarded as a balanced measure which can be used even if the classes are of very different sizes. The MCC is in essence a correlation coefficient value between -1 and +1. A coefficient of +1 represents a perfect prediction, 0 an average random prediction and -1 an inverse prediction. The statistic is also known as the phi coefficient. [source: Wikipedia]

Binary and multiclass labels are supported. Only in the binary case does this relate to information about true and false positives and negatives. See references below.

Read more in the User Guide.

Parameters:

y_true : array, shape = [n_samples]

Ground truth (correct) target values.

y_pred : array, shape = [n_samples]

Estimated targets as returned by a classifier.

sample_weight : array-like of shape = [n_samples], default None

Sample weights.

Returns:

mcc : float

The Matthews correlation coefficient (+1 represents a perfect prediction, 0 an average random prediction and -1 and inverse prediction).

References

[R221]

Baldi, Brunak, Chauvin, Andersen and Nielsen, (2000). Assessing the accuracy of prediction algorithms for classification: an overview

[R222]

Wikipedia entry for the Matthews Correlation Coefficient

[R223]

Gorodkin, (2004). Comparing two K-category assignments by a K-category correlation coefficient

[R224]

Jurman, Riccadonna, Furlanello, (2012). A Comparison of MCC and CEN Error Measures in MultiClass Prediction

Examples

>>> from sklearn.metrics import matthews_corrcoef
>>> y_true = [+1, +1, +1, -1]
>>> y_pred = [+1, -1, +1, +1]
>>> matthews_corrcoef(y_true, y_pred)  
-0.33...