Expand description
This crate implements a number of metrics such as MeanSquaredError and Accuracy.
Structs§
- Accuracy
- The accuracy is the proportion of examples where predicted == label.
- AucRoc
- This
Metriccomputes the area under the receiver operating characteristic curve. - Binary
Classification Metrics BinaryClassificationMetricscomputes common metrics used to evaluate binary classifiers at a number of classification thresholds.- Binary
Classification Metrics Input - The input to
BinaryClassificationMetrics. - Binary
Classification Metrics Output - BinaryClassificationMetrics contains common metrics used to evaluate binary classifiers.
- Binary
Classification Metrics Output ForThreshold - The output from
BinaryClassificationMetrics. - Binary
Cross Entropy - BinaryCrossEntropy is the loss function used in binary classification. Learn more.
- Binary
Cross Entropy Input - The input to
BinaryCrossEntropy. - Class
Metrics - ClassMetrics are class specific metrics used to evaluate the model’s performance on each individual class.
- Cross
Entropy - CrossEntropy is the loss function used in multiclass classification. Learn more.
- Cross
Entropy Input - The input to
CrossEntropy. - Cross
Entropy Output - The output from
CrossEntropy. - Mean
- The Mean metric is computed using Welford’s algorithm to ensure numeric stability.
- Mean
Squared Error - The mean squared error is the sum of squared differences between the predicted value and the label.
- Mean
Variance - Mode
- Multiclass
Classification Metrics - MulticlassClassificationMetrics computes common metrics used to evaluate multiclass classifiers.
- Multiclass
Classification Metrics Input - The input to
MulticlassClassificationMetrics. - Multiclass
Classification Metrics Output - The output from
MulticlassClassificationMetrics. - Regression
Metrics - RegressionMetrics computes metrics used to evaluate regressors.
- Regression
Metrics Input - The input to
RegressionMetrics. - Regression
Metrics Output - The output from
RegressionMetrics.
Functions§
- m2_
to_ variance - This function computes the variance given the
m2andn. - merge_
mean_ m2 - This function combines two separate means and variances into a single mean and variance which is useful in parallel algorithms.