Class: stats

stats

Measure the difference between the values predicted by a neural network and the observed values.

let expAndPredVals = [
  [[0.0, 0.0, 1.0], [0.0, 0.1, 0.9]],
  [[0.0, 1.0, 0.0], [0.8, 0.2, 0.0]],
  [[1.0, 0.0, 0.0], [0.7, 0.1, 0.2]],
  [[1.0, 0.0, 0.0], [0.3, 0.3, 0.4]],
  [[0.0, 0.0, 1.0], [0.2, 0.2, 0.6]]
];

Calculate the root mean square error:

rmse(expAndPredVals);

Calculate the score of the classification accuracy:

score(expAndPredVals);
Source:

Methods

(static) rmse(outputPairs)

Root Mean Square Error.

RMSE is the standard deviation of the prediction errors.

Parameters:
Name Type Description
outputPairs

An iterable of array-pairs that contain the expected and predicted values.
Source:
Returns:

The value of the RMSE metric.

(static) score(outputPairs)

Classification Accuracy.

The ratio of number of correct predictions to the total number of provided pairs. For a prediction to be considered as correct, the index of its maximum expected value needs to be the same with the index of its maximum predicted value.

Parameters:
Name Type Description
outputPairs

An iterable of array-pairs that contain the expected and predicted values.
Source:
Returns:

The score of the classification accuracy.