ml-roc-multiclass

Index

Functions

Functions

getAuc

getAuc(curves: Curve[]): number

- Defined in getAuc.ts:11
Returns the Area under the curve.

Parameters
- curves: Curve[]
  
  Object containing the true positivie and false positive rate vectors.
Returns number
Area under the curve.

getBinaryClassifiers

getBinaryClassifiers(responses: string[], predictions: number[]): { falseNegatives: number[]; falsePositives: number[]; trueNegatives: number[]; truePositives: number[] }

- Defined in getBinaryClassifiers.ts:10
Returns a ROC (Receiver Operating Characteristic) curve for a given response and prediction vectors.

Parameters
- responses: string[]
  
  Array containing category metadata.
- predictions: number[]
  
  Array containing the results of regression.
Returns { falseNegatives: number[]; falsePositives: number[]; trueNegatives: number[]; truePositives: number[] }
sensitivities and specificities as a object.
- falseNegatives: number[]
- falsePositives: number[]
- trueNegatives: number[]
- truePositives: number[]

getClasses

getClasses(array: string[]): Class[]

- Defined in utilities/getClasses.ts:7
Parameters
- array: string[]
  
  Array containing category metadata
Returns Class[]
Class object.

getRocCurve

getRocCurve(responses: string[], predictions: number[]): Curve[]

- Defined in getRocCurve.ts:14
Returns a ROC (Receiver Operating Characteristic) curve for a given response and prediction vectors.

Parameters
- responses: string[]
  
  Array containing category metadata.
- predictions: number[]
  
  Array containing the results of regression.
Returns Curve[]
sensitivities and specificities as a object.