sklearn

Use Python's #1 machine learning library from Node.js
scikitlearnsklearnmachine learningmlaiSelectorMixinCalibratedClassifierCVAffinityPropagationAgglomerativeClusteringBirchDBSCANFeatureAgglomerationKMeansBisectingKMeansMiniBatchKMeansMeanShiftOPTICSSpectralClusteringSpectralBiclusteringSpectralCoclusteringColumnTransformerTransformedTargetRegressorEmpiricalCovarianceEllipticEnvelopeGraphicalLassoGraphicalLassoCVLedoitWolfMinCovDetOASShrunkCovarianceCCAPLSCanonicalPLSRegressionPLSSVDDictionaryLearningFactorAnalysisFastICAIncrementalPCAKernelPCALatentDirichletAllocationMiniBatchDictionaryLearningMiniBatchSparsePCANMFMiniBatchNMFPCASparsePCASparseCoderTruncatedSVDLinearDiscriminantAnalysisQuadraticDiscriminantAnalysisDummyClassifierDummyRegressorAdaBoostClassifierAdaBoostRegressorBaggingClassifierBaggingRegressorExtraTreesClassifierExtraTreesRegressorGradientBoostingClassifierGradientBoostingRegressorIsolationForestRandomForestClassifierRandomForestRegressorRandomTreesEmbeddingStackingClassifierStackingRegressorVotingClassifierVotingRegressorHistGradientBoostingRegressorHistGradientBoostingClassifierConvergenceWarningDataConversionWarningDataDimensionalityWarningEfficiencyWarningFitFailedWarningNotFittedErrorUndefinedMetricWarningDictVectorizerFeatureHasherPatchExtractorCountVectorizerHashingVectorizerTfidfTransformerTfidfVectorizerGenericUnivariateSelectSelectPercentileSelectKBestSelectFprSelectFdrSelectFromModelSelectFweSequentialFeatureSelectorRFERFECVVarianceThresholdGaussianProcessClassifierGaussianProcessRegressorCompoundKernelConstantKernelDotProductExpSineSquaredExponentiationHyperparameterKernelMaternPairwiseKernelProductRBFRationalQuadraticSumWhiteKernelSimpleImputerIterativeImputerMissingIndicatorKNNImputerDecisionBoundaryDisplayPartialDependenceDisplayIsotonicRegressionAdditiveChi2SamplerNystroemPolynomialCountSketchRBFSamplerSkewedChi2SamplerKernelRidgeLogisticRegressionLogisticRegressionCVPassiveAggressiveClassifierPerceptronRidgeClassifierRidgeClassifierCVSGDClassifierSGDOneClassSVMLinearRegressionRidgeRidgeCVSGDRegressorElasticNetElasticNetCVLarsLarsCVLassoLassoCVLassoLarsLassoLarsCVLassoLarsICOrthogonalMatchingPursuitOrthogonalMatchingPursuitCVARDRegressionBayesianRidgeMultiTaskElasticNetMultiTaskElasticNetCVMultiTaskLassoMultiTaskLassoCVHuberRegressorQuantileRegressorRANSACRegressorTheilSenRegressorPoissonRegressorTweedieRegressorGammaRegressorIsomapLocallyLinearEmbeddingMDSSpectralEmbeddingTSNEDistanceMetricConfusionMatrixDisplayDetCurveDisplayPrecisionRecallDisplayPredictionErrorDisplayRocCurveDisplayCalibrationDisplayBayesianGaussianMixtureGaussianMixtureGroupKFoldGroupShuffleSplitKFoldLeaveOneGroupOutLeavePGroupsOutLeaveOneOutLeavePOutPredefinedSplitRepeatedKFoldRepeatedStratifiedKFoldShuffleSplitStratifiedKFoldStratifiedShuffleSplitStratifiedGroupKFoldTimeSeriesSplitGridSearchCVHalvingGridSearchCVParameterGridParameterSamplerRandomizedSearchCVHalvingRandomSearchCVLearningCurveDisplayOneVsRestClassifierOneVsOneClassifierOutputCodeClassifierClassifierChainMultiOutputRegressorMultiOutputClassifierRegressorChainBernoulliNBCategoricalNBComplementNBGaussianNBMultinomialNBBallTreeKDTreeKernelDensityKNeighborsClassifierKNeighborsRegressorKNeighborsTransformerLocalOutlierFactorRadiusNeighborsClassifierRadiusNeighborsRegressorRadiusNeighborsTransformerNearestCentroidNearestNeighborsNeighborhoodComponentsAnalysisBernoulliRBMMLPClassifierMLPRegressorFeatureUnionPipelineBinarizerFunctionTransformerKBinsDiscretizerKernelCentererLabelBinarizerLabelEncoderMultiLabelBinarizerMaxAbsScalerMinMaxScalerNormalizerOneHotEncoderOrdinalEncoderPolynomialFeaturesPowerTransformerQuantileTransformerRobustScalerSplineTransformerStandardScalerGaussianRandomProjectionSparseRandomProjectionLabelPropagationLabelSpreadingSelfTrainingClassifierLinearSVCLinearSVRNuSVCNuSVROneClassSVMSVCSVRDecisionTreeClassifierDecisionTreeRegressorExtraTreeClassifierExtraTreeRegressorBunchParallel

nestjs-early-access

This Nest Js package makes it easy to add early access mode to your existing application. This is useful for when you want to launch a product and need to gather the email addresses of people who want early access to the application.