## Description

Computes the Matthews Correlation Coefficient.

## Usage

 ```1 2 3 4 5``` ```metric_mcc( num_classes = NULL, name = "MatthewsCorrelationCoefficient", dtype = tf\$float32 ) ```

## Arguments

 `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'.

## Details

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:

## Value

Matthews correlation coefficient: float

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```## Not run: 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 ## End(Not run) ```

tfaddons documentation built on July 2, 2020, 2:12 a.m.