KNeighborClassifier
KNeighborClassifier is
Import
import * as Datacook from 'datacook';
const { KNeighborClassifier } = DataCook.Model;
Constructor
const kNeighborClassifier = new KNeighborClassifier({ nNeighbors: 4, leafSize: 3 });
Option Parameters
| parameter | type | description |
|---|---|---|
| nNeighbors | number | number of neighbors for each sample |
| leafSize | number | number of samples in each tree node for BallTree or KDTree |
| weights | “uniform” | “distance” | weights methods for generating classification. uniform: uniform weights;distance: weight points for inverse of their distance. Closer neighbor will get higher weights in this case. |
| metric | “euclidean” | “manhattan” | “minkowski” | metrics for computing distance. euclidean: euclidean distance; manhattan: manhattan distance; minkowski minkowski distance |
| p | number | power parameter for Minkowski metric |
Methods
fit
Syntax
async fit(xData: number[][] | Tensor2D, yData: number[] | string[] | boolean[] | Tensor1D): Promise<void>
Parameters
| Parameter | type | description |
|---|---|---|
| xData | Tensor2D| number[][] | input data of shape (nSamples,nFeatures) in type of array or tensor |
| yData | Tensor1D| number[] | string[] | boolean[] | input target |
predict
Make predictions using gradient boosting model.
async predict(xData: Tensor|RecursiveArray<number>): Promise<Tensor>
Parameters
| parameter | type | description |
|---|---|---|
| xData | Tensor | RecursiveArray <number> |
Returns
Promise of fitted values
fromJson
Load model paramters from json string object
async fromJson(modelJson: string)
Parameters
| parameter | type | description |
|---|---|---|
| modelJson | string | model json string |
toJson
Export model paramters to json string
async toJson(): Promise<string>
Returns
String output of model json