sklearn.metrics.matthews_corrcoef

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

Compute the Matthews correlation coefficient (MCC) for binary classes

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]

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

[R218]

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

[R219]

Wikipedia entry for the Matthews Correlation Coefficient

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...