Computes the Matthews Correlation Coefficient.
metric_mcc( num_classes = NULL, name = "MatthewsCorrelationCoefficient", dtype = tf$float32 )
| num_classes | Number of unique classes in the dataset. |
|---|---|
| name | (Optional) String name of the metric instance. |
| dtype | (Optional) Data type of the metric result. Defaults to `tf$float32`. |
Matthews correlation coefficient: float
The statistic is also known as the phi coefficient. The Matthews correlation coefficient (MCC) is used in machine learning as a measure of the quality of binary and multiclass 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 correlation coefficient value of MCC is 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. MCC = (TP * TN) - (FP * FN) / ((TP + FP) * (TP + FN) * (TN + FP ) * (TN + FN))^(1/2) Usage:
if (FALSE) { actuals = tf$constant(list(1, 1, 1, 0), dtype=tf$float32) preds = tf$constant(list(1,0,1,1), dtype=tf$float32) # Matthews correlation coefficient mcc = metric_mcc(num_classes=1) mcc$update_state(actuals, preds) paste('Matthews correlation coefficient is:', mcc$result()$numpy()) # Matthews correlation coefficient is : -0.33333334 }