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.

MachineShop: Machine Learning Models and Tools for R

Description

MachineShop is a meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Support is provided for predictive modeling of numerical, categorical, and censored time-to-event outcomes and for resample (bootstrap, cross-validation, and split training-test sets) estimation of model performance. This vignette introduces the package interface with a survival data analysis example, followed by supported methods of variable specification; applications to other response variable types; available performance metrics, resampling techniques, and graphical and tabular summaries; and modeling strategies.

Features

• Unified and concise interface for model fitting, prediction, and performance assessment.
• Support for 53+ models from 28 R packages, including model specifications from the parsnip package.
• Dynamic model parameters.
• Ensemble modeling with stacked regression and super learners.
• Modeling of response variables types: binary factors, multi-class nominal and ordinal factors, numeric vectors and matrices, and censored time-to-event survival.
• Model specification with traditional formulas, design matrices, and flexible pre-processing recipes.
• Resample estimation of predictive performance, including cross-validation, bootstrap resampling, and split training-test set validation.
• Parallel execution of resampling algorithms.
• Choices of performance metrics: accuracy, areas under ROC and precision recall curves, Brier score, coefficient of determination (R²), concordance index, cross entropy, F score, Gini coefficient, unweighted and weighted Cohen’s kappa, mean absolute error, mean squared error, mean squared log error, positive and negative predictive values, precision and recall, and sensitivity and specificity.
• Graphical and tabular performance summaries: calibration curves, confusion matrices, partial dependence plots, performance curves, lift curves, and model-specific and permutation-based variable importance.
• Model tuning over automatically generated grids and with exhaustive and random grid searches, Bayesian optimization, particle swarm optimization, quasi-Newton BFGS optimization, simulated annealing, and support for user-defined optimization functions.
• Model selection and comparisons for any combination of models and model parameter values.
• Recursive feature elimination.
• User-definable models and performance metrics.

Getting Started

Installation

# Current release from CRAN
install.packages("MachineShop")

# Development version from GitHub
# install.packages("pak")
pak::pak("brian-j-smith/MachineShop@develop")

Documentation

Once installed, the following R commands will load the package and display its help system documentation. Online documentation and examples are available at the MachineShop website.

library(MachineShop)

# Package help summary
?MachineShop