metric_mcc: MatthewsCorrelationCoefficient

In tfaddons: Interface to 'TensorFlow SIG Addons'

Description

Computes the Matthews Correlation Coefficient.

Usage

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

## 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.