MachineShop-package: MachineShop: Machine Learning Models and Tools

In MachineShop: Machine Learning Models and Tools

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.

Details

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

Training:

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

plot

print

summary

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.

Author(s)

Maintainer: Brian J Smith [e-mail]

See Also

Useful links:

• https://brian-j-smith.github.io/MachineShop/

• Report bugs at https://github.com/brian-j-smith/MachineShop/issues

MachineShop documentation built on Sept. 18, 2023, 5:06 p.m.