Package: MachineShop 3.8.0

MachineShop: Machine Learning Models and Tools

Meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Approaches for model fitting and prediction of numerical, categorical, or censored time-to-event outcomes include traditional regression models, regularization methods, tree-based methods, support vector machines, neural networks, ensembles, data preprocessing, filtering, and model tuning and selection. Performance metrics are provided for model assessment and can be estimated with independent test sets, split sampling, cross-validation, or bootstrap resampling. Resample estimation can be executed in parallel for faster processing and nested in cases of model tuning and selection. Modeling results can be summarized with descriptive statistics; calibration curves; variable importance; partial dependence plots; confusion matrices; and ROC, lift, and other performance curves.

Authors:Brian J Smith [aut, cre]

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MachineShop.pdf |MachineShop.html
MachineShop/json (API)
NEWS

# Install 'MachineShop' in R:
install.packages('MachineShop', repos = c('https://brian-j-smith.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/brian-j-smith/machineshop/issues

Datasets:
  • ICHomes - Iowa City Home Sales Dataset

On CRAN:

classification-modelsmachine-learningpredictive-modelingregression-modelssurvival-models

8.33 score 62 stars 123 scripts 718 downloads 4 mentions 154 exports 95 dependencies

Last updated 3 months agofrom:3face9df07. Checks:OK: 6 NOTE: 3. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 18 2024
R-4.5-win-x86_64OKOct 18 2024
R-4.5-linux-x86_64OKOct 18 2024
R-4.4-win-x86_64OKOct 18 2024
R-4.4-mac-x86_64OKOct 18 2024
R-4.4-mac-aarch64OKOct 18 2024
R-4.3-win-x86_64NOTEOct 18 2024
R-4.3-mac-x86_64NOTEOct 18 2024
R-4.3-mac-aarch64NOTEOct 18 2024

Exports:.%>%accuracyAdaBagModelAdaBoostModelas.MLInputas.MLModelaucBARTMachineModelBARTModelBinomialVariateBlackBoostModelBootControlBootOptimismControlbrierC50Modelcalibrationcase_weightsCForestModelcindexconfusionConfusionMatrixCoxModelCoxStepAICModelcross_entropyCVControlCVOptimismControldependenceDiscreteVariateEarthModelexpand_modelexpand_modelgridexpand_paramsexpand_stepsf_scoreFDAModelfitfnrfprGAMBoostModelGBMModelginiGLMBoostModelGLMModelGLMNetModelGLMStepAICModelkappa2KNNModelLARSModelLDAModelliftLMModelmaeMDAModelmetricinfoMLMetricMLMetric<-MLModelMLModelFunctionModelFramemodelinfoModelSpecificationmsemsleNaiveBayesModelNegBinomialVariateNNetModelnpvOOBControlParameterGridParsnipModelPDAModelperformanceperformance_curvePLSModelPoissonVariatePOLRModelpprppvpr_aucprecisionpredictQDAModelr2RandomForestModelRangerModelrecallresampleresponserfeRFSRCFastModelRFSRCModelrmsermsleroc_aucroc_indexrole_binomrole_caserole_predrole_survRPartModelSelectedInputSelectedModelsensitivityset_monitorset_optim_bayesset_optim_bfgsset_optim_gridset_optim_methodset_optim_psoset_optim_sannset_predictset_stratasettingsspecificitySplitControlStackedModelstep_kmeansstep_kmedoidsstep_lincompstep_sbfstep_spcaSuperModelSurvEventsSurvProbsSurvRegModelSurvRegStepAICModelSVMANOVAModelSVMBesselModelSVMLaplaceModelSVMLinearModelSVMModelSVMPolyModelSVMRadialModelSVMSplineModelSVMTanhModeltnrtprTrainControlTreeModeltunable.step_kmeanstunable.step_kmedoidstunable.step_lincomptunable.step_spcaTunedInputTunedModelTuningGridunMLModelFitvarimpweighted_kappa2XGBDARTModelXGBLinearModelXGBModelXGBTreeModel

Dependencies:abindclasscliclockcodetoolscoincolorspacecpp11crayondata.tablediagramdialsDiceDesigndigestdplyrfansifarverforeachfurrrfuturefuture.applygenericsggplot2globalsgluegowergtablehardhathmsipredisobanditeratorskernlabKernSmoothlabelinglatticelavalibcoinlifecyclelistenvlubridatemagrittrMASSMatrixmatrixStatsmgcvmodeltoolsmultcompmunsellmvtnormnlmennetnumDerivparallellypartypillarpkgconfigpolsplineprettyunitsprodlimprogressprogressrpurrrR6RColorBrewerRcpprecipesrlangrpartrsampleRsolnpsandwichscalessfdshapesliderSQUAREMstringistringrstrucchangesurvivalTH.datatibbletidyrtidyselecttimechangetimeDatetruncnormtzdbutf8vctrsviridisLitewarpwithrzoo

Conventions for MLModels Implementation

Rendered fromMLModels.Rmdusingknitr::rmarkdownon Oct 18 2024.

Last update: 2021-11-30
Started: 2018-09-25

MachineShop User Guide

Rendered fromUserGuide.Rmdusingknitr::rmarkdownon Oct 18 2024.

Last update: 2024-07-23
Started: 2021-11-30

Readme and manuals

Help Manual

Help pageTopics
MachineShop: Machine Learning Models and ToolsMachineShop-package MachineShop
Bagging with Classification TreesAdaBagModel
Boosting with Classification TreesAdaBoostModel
Coerce to a Data Frameas.data.frame as.data.frame.ModelFrame as.data.frame.Resample as.data.frame.TabularArray
Coerce to an MLInputas.MLInput as.MLInput.MLModelFit as.MLInput.ModelSpecification
Coerce to an MLModelas.MLModel as.MLModel.MLModelFit as.MLModel.ModelSpecification as.MLModel.model_spec
Bayesian Additive Regression Trees ModelBARTMachineModel
Bayesian Additive Regression Trees ModelBARTModel
Gradient Boosting with Regression TreesBlackBoostModel
C5.0 Decision Trees and Rule-Based ModelC50Model
Model Calibrationcalibration
Extract Case Weightscase_weights
Conditional Random Forest ModelCForestModel
Combine MachineShop Objects+,SurvMatrix,SurvMatrix-method c c.Calibration c.ConfusionList c.ConfusionMatrix c.LiftCurve c.ListOf c.PerformanceCurve c.Resample combine
Confusion Matrixconfusion ConfusionMatrix
Proportional Hazards Regression ModelCoxModel CoxStepAICModel
Partial Dependencedependence
Model Performance Differencesdiff diff.MLModel diff.Performance diff.Resample
Discrete Variate ConstructorsBinomialVariate DiscreteVariate NegBinomialVariate PoissonVariate
Multivariate Adaptive Regression Splines ModelEarthModel
Model Expansion Over Tuning Parametersexpand_model
Model Tuning Grid Expansionexpand_modelgrid expand_modelgrid.formula expand_modelgrid.matrix expand_modelgrid.MLModel expand_modelgrid.MLModelFunction expand_modelgrid.ModelFrame expand_modelgrid.ModelSpecification expand_modelgrid.recipe
Model Parameters Expansionexpand_params
Recipe Step Parameters Expansionexpand_steps
Extract Elements of an Objectextract [,DiscreteVariate,ANY,missing,missing-method [,ListOf,ANY,missing,missing-method [,ModelFrame,ANY,ANY,ANY-method [,ModelFrame,ANY,missing,ANY-method [,ModelFrame,missing,ANY,ANY-method [,ModelFrame,missing,missing,ANY-method [,RecipeGrid,ANY,ANY,ANY-method [,Resample,ANY,ANY,ANY-method [,Resample,ANY,missing,ANY-method [,Resample,missing,missing,ANY-method [,SurvMatrix,ANY,ANY,ANY-method [,SurvTimes,ANY,missing,missing-method [.BinomialVariate
Flexible and Penalized Discriminant Analysis ModelsFDAModel PDAModel
Model Fittingfit fit.formula fit.matrix fit.MLModel fit.MLModelFunction fit.ModelFrame fit.ModelSpecification fit.recipe
Gradient Boosting with Additive ModelsGAMBoostModel
Generalized Boosted Regression ModelGBMModel
Gradient Boosting with Linear ModelsGLMBoostModel
Generalized Linear ModelGLMModel GLMStepAICModel
GLM Lasso or Elasticnet ModelGLMNetModel
Iowa City Home Sales DatasetICHomes
Model Inputsinputs
Weighted k-Nearest Neighbor ModelKNNModel
Least Angle Regression, Lasso and Infinitesimal Forward Stagewise ModelsLARSModel
Linear Discriminant Analysis ModelLDAModel
Model Lift Curveslift
Linear ModelsLMModel
Mixture Discriminant Analysis ModelMDAModel
Display Performance Metric Informationmetricinfo
Performance Metricsaccuracy auc brier cindex cross_entropy fnr fpr f_score gini kappa2 mae metrics mse msle npv ppr ppv precision pr_auc r2 recall rmse rmsle roc_auc roc_index sensitivity specificity tnr tpr weighted_kappa2
Resampling ControlsBootControl BootOptimismControl controls CVControl CVOptimismControl MLControl OOBControl SplitControl TrainControl
MLMetric Class ConstructorMLMetric MLMetric<-
MLModel and MLModelFunction Class ConstructorsMLModel MLModelFunction
ModelFrame ClassModelFrame ModelFrame.formula ModelFrame.matrix
Display Model Informationmodelinfo
Modelsmodels
Model SpecificationModelSpecification ModelSpecification.default ModelSpecification.formula ModelSpecification.matrix ModelSpecification.ModelFrame ModelSpecification.recipe
Naive Bayes Classifier ModelNaiveBayesModel
Neural Network ModelNNetModel
Tuning Parameters GridParameterGrid ParameterGrid.list ParameterGrid.param ParameterGrid.parameters
Parsnip ModelParsnipModel
Model Performance Metricsperformance performance.BinomialVariate performance.ConfusionList performance.ConfusionMatrix performance.factor performance.matrix performance.MLModel performance.numeric performance.Resample performance.Surv performance.TrainingStep
Model Performance Curvescurves performance_curve performance_curve.default performance_curve.Resample
Model Performance Plotsplot plot.Calibration plot.ConfusionList plot.ConfusionMatrix plot.LiftCurve plot.MLModel plot.PartialDependence plot.Performance plot.PerformanceCurve plot.Resample plot.TrainingStep plot.VariableImportance
Partial Least Squares ModelPLSModel
Ordered Logistic or Probit Regression ModelPOLRModel
Model Predictionpredict predict,MLModelFit-method predict.MLModelFit
Print MachineShop Objectsprint print.BinomialVariate print.Calibration print.DiscreteVariate print.ListOf print.MLControl print.MLMetric print.MLModel print.MLModelFunction print.ModelFrame print.ModelRecipe print.ModelSpecification print.Performance print.PerformanceCurve print.RecipeGrid print.Resample print.SurvMatrix print.SurvTimes print.TrainingStep print.VariableImportance
Quadratic Discriminant Analysis ModelQDAModel
Quote Operator. quote
Random Forest ModelRandomForestModel
Fast Random Forest ModelRangerModel
Set Recipe Rolesrecipe_roles role_binom role_case role_pred role_surv
Resample Estimation of Model Performanceresample resample.formula resample.matrix resample.MLModel resample.MLModelFunction resample.ModelFrame resample.ModelSpecification resample.recipe
Extract Response Variableresponse response.MLModelFit response.ModelFrame response.ModelSpecification response.recipe
Recursive Feature Eliminationrfe rfe.formula rfe.matrix rfe.MLModel rfe.MLModelFunction rfe.ModelFrame rfe.ModelSpecification rfe.recipe
Fast Random Forest (SRC) ModelRFSRCFastModel RFSRCModel
Recursive Partitioning and Regression Tree ModelsRPartModel
Selected Model InputsSelectedInput SelectedInput.formula SelectedInput.list SelectedInput.matrix SelectedInput.ModelFrame SelectedInput.ModelSpecification SelectedInput.recipe SelectedModelFrame SelectedModelRecipe SelectedModelSpecification
Selected ModelSelectedModel SelectedModel.default SelectedModel.list SelectedModel.ModelSpecification
Training Parameters Monitoring Controlset_monitor set_monitor.MLControl set_monitor.MLOptimization set_monitor.ModelSpecification
Tuning Parameter Optimizationset_optim set_optim_bayes set_optim_bayes.ModelSpecification set_optim_bfgs set_optim_bfgs.ModelSpecification set_optim_grid set_optim_grid.ModelSpecification set_optim_grid.TrainingParams set_optim_grid.TunedInput set_optim_grid.TunedModel set_optim_method set_optim_method.ModelSpecification set_optim_pso set_optim_pso.ModelSpecification set_optim_sann set_optim_sann.ModelSpecification
Resampling Prediction Controlset_predict
Resampling Stratification Controlset_strata
MachineShop Settingssettings
Stacked Regression ModelStackedModel
K-Means Clustering Variable Reductionstep_kmeans tidy.step_kmeans tunable.step_kmeans
K-Medoids Clustering Variable Selectionstep_kmedoids tunable.step_kmedoids
Linear Components Variable Reductionstep_lincomp tidy.step_lincomp tunable.step_lincomp
Variable Selection by Filteringstep_sbf tidy.step_sbf
Sparse Principal Components Analysis Variable Reductionstep_spca tunable.step_spca
Model Performance Summariessummary summary.ConfusionList summary.ConfusionMatrix summary.MLModel summary.MLModelFit summary.Performance summary.PerformanceCurve summary.Resample summary.TrainingStep
Super Learner ModelSuperModel
SurvMatrix Class ConstructorsSurvEvents SurvMatrix SurvProbs
Parametric Survival ModelSurvRegModel SurvRegStepAICModel
Support Vector Machine ModelsSVMANOVAModel SVMBesselModel SVMLaplaceModel SVMLinearModel SVMModel SVMPolyModel SVMRadialModel SVMSplineModel SVMTanhModel
Paired t-Tests for Model Comparisonst.test t.test.PerformanceDiff
Classification and Regression Tree ModelsTreeModel
Tuned Model InputsTunedInput TunedInput.recipe TunedModelRecipe
Tuned ModelTunedModel
Tuning Grid ControlTuningGrid
Revert an MLModelFit ObjectunMLModelFit
Variable Importancevarimp
Extreme Gradient Boosting ModelsXGBDARTModel XGBLinearModel XGBModel XGBTreeModel