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MachineShop (version 3.5.0)

MachineShop-package: MachineShop: Machine Learning Models and Tools

Description

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.

Arguments

Author

Maintainer: Brian J Smith brian-j-smith@uiowa.edu

Details

The following set of model fitting, prediction, and performance assessment functions are available for MachineShop models.

Training:

fitModel fitting
resampleResample estimation of model performance

Tuning Grids:

expand_modelModel expansion over tuning parameters
expand_modelgridModel tuning grid expansion
expand_paramsModel parameters expansion
expand_stepsRecipe step parameters expansion

Response Values:

responseObserved
predictPredicted

Performance Assessment:

calibrationModel calibration
confusionConfusion matrix
dependenceParital dependence
diffModel performance differences
liftLift curves
performance metricsModel performance metrics
performance_curveModel performance curves
rfeRecursive feature elimination
varimpVariable importance

Methods for resample estimation include

BootControlSimple bootstrap
BootOptimismControlOptimism-corrected bootstrap
CVControlRepeated K-fold cross-validation
CVOptimismControlOptimism-corrected cross-validation
OOBControlOut-of-bootstrap
SplitControlSplit training-testing
TrainControlTraining resubstitution

Graphical and tabular summaries of modeling results can be obtained with

plot
print
summary

Further information on package features is available with

metricinfoPerformance metric information
modelinfoModel information
settingsGlobal settings

Custom metrics and models can be created with the MLMetric and MLModel constructors.

See Also