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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...
sklearn.metrics.matthews_corrcoef()
2017-01-15 04:26:27
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