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:
MachineShop_3.8.0.tar.gz
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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')) |
Bug tracker:https://github.com/brian-j-smith/machineshop/issues
- ICHomes - Iowa City Home Sales Dataset
classification-modelsmachine-learningpredictive-modelingregression-modelssurvival-models
Last updated 3 months agofrom:3face9df07. Checks:OK: 6 NOTE: 3. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 17 2024 |
R-4.5-win-x86_64 | OK | Nov 17 2024 |
R-4.5-linux-x86_64 | OK | Nov 17 2024 |
R-4.4-win-x86_64 | OK | Nov 17 2024 |
R-4.4-mac-x86_64 | OK | Nov 17 2024 |
R-4.4-mac-aarch64 | OK | Nov 17 2024 |
R-4.3-win-x86_64 | NOTE | Nov 17 2024 |
R-4.3-mac-x86_64 | NOTE | Nov 17 2024 |
R-4.3-mac-aarch64 | NOTE | Nov 17 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
Readme and manuals
Help Manual
Help page | Topics |
---|---|
MachineShop: Machine Learning Models and Tools | MachineShop-package MachineShop |
Bagging with Classification Trees | AdaBagModel |
Boosting with Classification Trees | AdaBoostModel |
Coerce to a Data Frame | as.data.frame as.data.frame.ModelFrame as.data.frame.Resample as.data.frame.TabularArray |
Coerce to an MLInput | as.MLInput as.MLInput.MLModelFit as.MLInput.ModelSpecification |
Coerce to an MLModel | as.MLModel as.MLModel.MLModelFit as.MLModel.ModelSpecification as.MLModel.model_spec |
Bayesian Additive Regression Trees Model | BARTMachineModel |
Bayesian Additive Regression Trees Model | BARTModel |
Gradient Boosting with Regression Trees | BlackBoostModel |
C5.0 Decision Trees and Rule-Based Model | C50Model |
Model Calibration | calibration |
Extract Case Weights | case_weights |
Conditional Random Forest Model | CForestModel |
Combine MachineShop Objects | +,SurvMatrix,SurvMatrix-method c c.Calibration c.ConfusionList c.ConfusionMatrix c.LiftCurve c.ListOf c.PerformanceCurve c.Resample combine |
Confusion Matrix | confusion ConfusionMatrix |
Proportional Hazards Regression Model | CoxModel CoxStepAICModel |
Partial Dependence | dependence |
Model Performance Differences | diff diff.MLModel diff.Performance diff.Resample |
Discrete Variate Constructors | BinomialVariate DiscreteVariate NegBinomialVariate PoissonVariate |
Multivariate Adaptive Regression Splines Model | EarthModel |
Model Expansion Over Tuning Parameters | expand_model |
Model Tuning Grid Expansion | expand_modelgrid expand_modelgrid.formula expand_modelgrid.matrix expand_modelgrid.MLModel expand_modelgrid.MLModelFunction expand_modelgrid.ModelFrame expand_modelgrid.ModelSpecification expand_modelgrid.recipe |
Model Parameters Expansion | expand_params |
Recipe Step Parameters Expansion | expand_steps |
Extract Elements of an Object | extract [,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 Models | FDAModel PDAModel |
Model Fitting | fit fit.formula fit.matrix fit.MLModel fit.MLModelFunction fit.ModelFrame fit.ModelSpecification fit.recipe |
Gradient Boosting with Additive Models | GAMBoostModel |
Generalized Boosted Regression Model | GBMModel |
Gradient Boosting with Linear Models | GLMBoostModel |
Generalized Linear Model | GLMModel GLMStepAICModel |
GLM Lasso or Elasticnet Model | GLMNetModel |
Iowa City Home Sales Dataset | ICHomes |
Model Inputs | inputs |
Weighted k-Nearest Neighbor Model | KNNModel |
Least Angle Regression, Lasso and Infinitesimal Forward Stagewise Models | LARSModel |
Linear Discriminant Analysis Model | LDAModel |
Model Lift Curves | lift |
Linear Models | LMModel |
Mixture Discriminant Analysis Model | MDAModel |
Display Performance Metric Information | metricinfo |
Performance Metrics | accuracy 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 Controls | BootControl BootOptimismControl controls CVControl CVOptimismControl MLControl OOBControl SplitControl TrainControl |
MLMetric Class Constructor | MLMetric MLMetric<- |
MLModel and MLModelFunction Class Constructors | MLModel MLModelFunction |
ModelFrame Class | ModelFrame ModelFrame.formula ModelFrame.matrix |
Display Model Information | modelinfo |
Models | models |
Model Specification | ModelSpecification ModelSpecification.default ModelSpecification.formula ModelSpecification.matrix ModelSpecification.ModelFrame ModelSpecification.recipe |
Naive Bayes Classifier Model | NaiveBayesModel |
Neural Network Model | NNetModel |
Tuning Parameters Grid | ParameterGrid ParameterGrid.list ParameterGrid.param ParameterGrid.parameters |
Parsnip Model | ParsnipModel |
Model Performance Metrics | performance performance.BinomialVariate performance.ConfusionList performance.ConfusionMatrix performance.factor performance.matrix performance.MLModel performance.numeric performance.Resample performance.Surv performance.TrainingStep |
Model Performance Curves | curves performance_curve performance_curve.default performance_curve.Resample |
Model Performance Plots | plot 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 Model | PLSModel |
Ordered Logistic or Probit Regression Model | POLRModel |
Model Prediction | predict predict,MLModelFit-method predict.MLModelFit |
Print MachineShop Objects | print 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 Model | QDAModel |
Quote Operator | . quote |
Random Forest Model | RandomForestModel |
Fast Random Forest Model | RangerModel |
Set Recipe Roles | recipe_roles role_binom role_case role_pred role_surv |
Resample Estimation of Model Performance | resample resample.formula resample.matrix resample.MLModel resample.MLModelFunction resample.ModelFrame resample.ModelSpecification resample.recipe |
Extract Response Variable | response response.MLModelFit response.ModelFrame response.ModelSpecification response.recipe |
Recursive Feature Elimination | rfe rfe.formula rfe.matrix rfe.MLModel rfe.MLModelFunction rfe.ModelFrame rfe.ModelSpecification rfe.recipe |
Fast Random Forest (SRC) Model | RFSRCFastModel RFSRCModel |
Recursive Partitioning and Regression Tree Models | RPartModel |
Selected Model Inputs | SelectedInput SelectedInput.formula SelectedInput.list SelectedInput.matrix SelectedInput.ModelFrame SelectedInput.ModelSpecification SelectedInput.recipe SelectedModelFrame SelectedModelRecipe SelectedModelSpecification |
Selected Model | SelectedModel SelectedModel.default SelectedModel.list SelectedModel.ModelSpecification |
Training Parameters Monitoring Control | set_monitor set_monitor.MLControl set_monitor.MLOptimization set_monitor.ModelSpecification |
Tuning Parameter Optimization | set_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 Control | set_predict |
Resampling Stratification Control | set_strata |
MachineShop Settings | settings |
Stacked Regression Model | StackedModel |
K-Means Clustering Variable Reduction | step_kmeans tidy.step_kmeans tunable.step_kmeans |
K-Medoids Clustering Variable Selection | step_kmedoids tunable.step_kmedoids |
Linear Components Variable Reduction | step_lincomp tidy.step_lincomp tunable.step_lincomp |
Variable Selection by Filtering | step_sbf tidy.step_sbf |
Sparse Principal Components Analysis Variable Reduction | step_spca tunable.step_spca |
Model Performance Summaries | summary summary.ConfusionList summary.ConfusionMatrix summary.MLModel summary.MLModelFit summary.Performance summary.PerformanceCurve summary.Resample summary.TrainingStep |
Super Learner Model | SuperModel |
SurvMatrix Class Constructors | SurvEvents SurvMatrix SurvProbs |
Parametric Survival Model | SurvRegModel SurvRegStepAICModel |
Support Vector Machine Models | SVMANOVAModel SVMBesselModel SVMLaplaceModel SVMLinearModel SVMModel SVMPolyModel SVMRadialModel SVMSplineModel SVMTanhModel |
Paired t-Tests for Model Comparisons | t.test t.test.PerformanceDiff |
Classification and Regression Tree Models | TreeModel |
Tuned Model Inputs | TunedInput TunedInput.recipe TunedModelRecipe |
Tuned Model | TunedModel |
Tuning Grid Control | TuningGrid |
Revert an MLModelFit Object | unMLModelFit |
Variable Importance | varimp |
Extreme Gradient Boosting Models | XGBDARTModel XGBLinearModel XGBModel XGBTreeModel |