Principal component analysis (PCA).
const{PCA}=require('ml-pca');constdataset=require('ml-dataset-iris').getNumbers();// dataset is a two-dimensional array where rows represent the samples and columns the featuresconstpca=newPCA(dataset);console.log(pca.getExplainedVariance());/*[ 0.9246187232017269, 0.05306648311706785, 0.017102609807929704, 0.005212183873275558 ]*/constnewPoints=[[4.9,3.2,1.2,0.4],[5.4,3.3,1.4,0.9],];console.log(pca.predict(newPoints));// project new points into the PCA space/*[ [ -2.830722471866897, 0.01139060953209596, 0.0030369648815961603, -0.2817812120420965 ], [ -2.308002707614927, -0.3175048770719249, 0.059976053412802766, -0.688413413360567 ]]*/
