MachineShop-package: MachineShop: Machine Learning Models and Tools

MachineShop-packageR Documentation

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.


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


fit Model fitting
resample Resample estimation of model performance

Tuning Grids:

expand_model Model expansion over tuning parameters
expand_modelgrid Model tuning grid expansion
expand_params Model parameters expansion
expand_steps Recipe step parameters expansion

Response Values:

response Observed
predict Predicted

Performance Assessment:

calibration Model calibration
confusion Confusion matrix
dependence Parital dependence
diff Model performance differences
lift Lift curves
performance metrics Model performance metrics
performance_curve Model performance curves
rfe Recursive feature elimination
varimp Variable importance

Methods for resample estimation include

BootControl Simple bootstrap
BootOptimismControl Optimism-corrected bootstrap
CVControl Repeated K-fold cross-validation
CVOptimismControl Optimism-corrected cross-validation
OOBControl Out-of-bootstrap
SplitControl Split training-testing
TrainControl Training resubstitution

Graphical and tabular summaries of modeling results can be obtained with


Further information on package features is available with

metricinfo Performance metric information
modelinfo Model information
settings Global settings

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


Maintainer: Brian J Smith

See Also

Useful links:

MachineShop documentation built on Sept. 5, 2022, 5:08 p.m.