Documentation
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Index ¶
- func GetAccuracy(c ConfusionMatrix) float64
- func GetConfusionMatrix(ref base.FixedDataGrid, gen base.FixedDataGrid) (map[string]map[string]int, error)
- func GetF1Score(class string, c ConfusionMatrix) float64
- func GetFalseNegatives(class string, c ConfusionMatrix) float64
- func GetFalsePositives(class string, c ConfusionMatrix) float64
- func GetMacroPrecision(c ConfusionMatrix) float64
- func GetMacroRecall(c ConfusionMatrix) float64
- func GetMicroPrecision(c ConfusionMatrix) float64
- func GetMicroRecall(c ConfusionMatrix) float64
- func GetPrecision(class string, c ConfusionMatrix) float64
- func GetRecall(class string, c ConfusionMatrix) float64
- func GetSummary(c ConfusionMatrix) string
- func GetTrueNegatives(class string, c ConfusionMatrix) float64
- func GetTruePositives(class string, c ConfusionMatrix) float64
- type ConfusionMatrix
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func GetAccuracy ¶
func GetAccuracy(c ConfusionMatrix) float64
GetAccuracy computes the overall classification accuracy That is (number of correctly classified instances) / total instances
func GetConfusionMatrix ¶
func GetConfusionMatrix(ref base.FixedDataGrid, gen base.FixedDataGrid) (map[string]map[string]int, error)
GetConfusionMatrix builds a ConfusionMatrix from a set of reference (`ref') and generate (`gen') Instances.
func GetF1Score ¶
func GetF1Score(class string, c ConfusionMatrix) float64
GetF1Score computes the harmonic mean of precision and recall (equivalently called F-measure)
func GetFalseNegatives ¶
func GetFalseNegatives(class string, c ConfusionMatrix) float64
GetFalseNegatives returns the number of times an entry is incorrectly predicted as something other than the given class.
func GetFalsePositives ¶
func GetFalsePositives(class string, c ConfusionMatrix) float64
GetFalsePositives returns the number of times an entry is incorrectly predicted as having a given class.
func GetMacroPrecision ¶
func GetMacroPrecision(c ConfusionMatrix) float64
GetMacroPrecision assesses Classifier performance across all classes by averaging the precision measures achieved for each class.
func GetMacroRecall ¶
func GetMacroRecall(c ConfusionMatrix) float64
GetMacroRecall assesses Classifier performance across all classes by averaging the recall measures achieved for each class
func GetMicroPrecision ¶
func GetMicroPrecision(c ConfusionMatrix) float64
GetMicroPrecision assesses Classifier performance across all classes using the total true positives and false positives.
func GetMicroRecall ¶
func GetMicroRecall(c ConfusionMatrix) float64
GetMicroRecall assesses Classifier performance across all classes using the total true positives and false negatives.
func GetPrecision ¶
func GetPrecision(class string, c ConfusionMatrix) float64
GetPrecision returns the fraction of of the total predictions for a given class which were correct.
func GetRecall ¶
func GetRecall(class string, c ConfusionMatrix) float64
GetRecall returns the fraction of the total occurrences of a given class which were predicted.
func GetSummary ¶
func GetSummary(c ConfusionMatrix) string
GetSummary returns a table of precision, recall, true positive, false positive, and true negatives for each class for a given ConfusionMatrix
func GetTrueNegatives ¶
func GetTrueNegatives(class string, c ConfusionMatrix) float64
GetTrueNegatives returns the number of times an entry is correctly predicted as something other than the given class.
func GetTruePositives ¶
func GetTruePositives(class string, c ConfusionMatrix) float64
GetTruePositives returns the number of times an entry is predicted successfully in a given ConfusionMatrix.
Types ¶
type ConfusionMatrix ¶
ConfusionMatrix is a nested map of actual and predicted class counts
Source Files