Namespace SiaNet.Metrics

Classes

Accuracy

Accuracy is suggested to use to measure how accurate is the overall performance of a model is, considering both positive and negative classes without worrying about the imbalance of a data set

BaseMetric

A metric is a function that is used to judge the performance of your model. Metric functions are to be supplied in the metrics parameter when a model is compiled.

BinaryAccuracy

Positive and negative predictive values. In addition to sensitivity and specificity, the performance of a binary classification test can be measured with positive predictive value (PPV), also known as precision, and negative predictive value (NPV)

MAE

Mean Absolute Error (MAE) is a quantity used to measure how close forecasts or predictions are to the eventual outcomes

MAPE

The mean absolute percentage error (MAPE), also known as mean absolute percentage deviation (MAPD), is a measure of prediction accuracy of a forecasting method in statistics, for example in trend estimation, also used as a Loss function for regression problems in Machine Learning.

MSE

The mean squared error (MSE) or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors—that is, the average squared difference between the estimated values and what is estimated.

MSLE

Mean squared logarithmic error (MSLE) is, as the name suggests, a variation of the Mean Squared Error.