R: Machine Learning Models and Tools

# Machine Learning Models and Tools

## Documentation for package ‘MachineShop’ version 3.9.2

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

A B C D E F G I K L M N O P Q R S T U V W X misc

MachineShop-package | MachineShop: Machine Learning Models and Tools

## -- A --

accuracy | Performance Metrics
AdaBagModel | Bagging with Classification Trees
AdaBoostModel | Boosting with Classification Trees
as.data.frame | Coerce to a Data Frame
as.data.frame.ModelFrame | Coerce to a Data Frame
as.data.frame.Resample | Coerce to a Data Frame
as.data.frame.TabularArray | Coerce to a Data Frame
as.MLInput | Coerce to an MLInput
as.MLInput.MLModelFit | Coerce to an MLInput
as.MLInput.ModelSpecification | Coerce to an MLInput
as.MLModel | Coerce to an MLModel
as.MLModel.MLModelFit | Coerce to an MLModel
as.MLModel.ModelSpecification | Coerce to an MLModel
as.MLModel.model_spec | Coerce to an MLModel
auc | Performance Metrics

## -- B --

BARTMachineModel | Bayesian Additive Regression Trees Model
BARTModel | Bayesian Additive Regression Trees Model
BinomialVariate | Discrete Variate Constructors
BlackBoostModel | Gradient Boosting with Regression Trees
BootControl | Resampling Controls
BootOptimismControl | Resampling Controls
brier | Performance Metrics

## -- C --

c | Combine MachineShop Objects
c.Calibration | Combine MachineShop Objects
c.ConfusionList | Combine MachineShop Objects
c.ConfusionMatrix | Combine MachineShop Objects
c.LiftCurve | Combine MachineShop Objects
c.ListOf | Combine MachineShop Objects
c.PerformanceCurve | Combine MachineShop Objects
c.Resample | Combine MachineShop Objects
C50Model | C5.0 Decision Trees and Rule-Based Model
calibration | Model Calibration
case_weights | Extract Case Weights
CForestModel | Conditional Random Forest Model
cindex | Performance Metrics
combine | Combine MachineShop Objects
confusion | Confusion Matrix
ConfusionMatrix | Confusion Matrix
controls | Resampling Controls
CoxModel | Proportional Hazards Regression Model
CoxStepAICModel | Proportional Hazards Regression Model
cross_entropy | Performance Metrics
curves | Model Performance Curves
CVControl | Resampling Controls
CVOptimismControl | Resampling Controls

## -- D --

dependence | Partial Dependence
diff | Model Performance Differences
diff.MLModel | Model Performance Differences
diff.Performance | Model Performance Differences
diff.Resample | Model Performance Differences
DiscreteVariate | Discrete Variate Constructors

## -- E --

EarthModel | Multivariate Adaptive Regression Splines Model
expand_model | Model Expansion Over Tuning Parameters
expand_modelgrid | Model Tuning Grid Expansion
expand_modelgrid.formula | Model Tuning Grid Expansion
expand_modelgrid.matrix | Model Tuning Grid Expansion
expand_modelgrid.MLModel | Model Tuning Grid Expansion
expand_modelgrid.MLModelFunction | Model Tuning Grid Expansion
expand_modelgrid.ModelFrame | Model Tuning Grid Expansion
expand_modelgrid.ModelSpecification | Model Tuning Grid Expansion
expand_modelgrid.recipe | Model Tuning Grid Expansion
expand_params | Model Parameters Expansion
expand_steps | Recipe Step Parameters Expansion
extract | Extract Elements of an Object

## -- F --

FDAModel | Flexible and Penalized Discriminant Analysis Models
fit | Model Fitting
fit.formula | Model Fitting
fit.matrix | Model Fitting
fit.MLModel | Model Fitting
fit.MLModelFunction | Model Fitting
fit.ModelFrame | Model Fitting
fit.ModelSpecification | Model Fitting
fit.recipe | Model Fitting
fnr | Performance Metrics
fpr | Performance Metrics
f_score | Performance Metrics

## -- G --

GAMBoostModel | Gradient Boosting with Additive Models
GBMModel | Generalized Boosted Regression Model
gini | Performance Metrics
GLMBoostModel | Gradient Boosting with Linear Models
GLMModel | Generalized Linear Model
GLMNetModel | GLM Lasso or Elasticnet Model
GLMStepAICModel | Generalized Linear Model

## -- I --

ICHomes | Iowa City Home Sales Dataset
inputs | Model Inputs

## -- K --

kappa2 | Performance Metrics
KNNModel | Weighted k-Nearest Neighbor Model

## -- L --

LARSModel | Least Angle Regression, Lasso and Infinitesimal Forward Stagewise Models
LDAModel | Linear Discriminant Analysis Model
lift | Model Lift Curves
LMModel | Linear Models

## -- M --

MachineShop | MachineShop: Machine Learning Models and Tools
mae | Performance Metrics
MDAModel | Mixture Discriminant Analysis Model
metricinfo | Display Performance Metric Information
metrics | Performance Metrics
MLControl | Resampling Controls
MLMetric | MLMetric Class Constructor
MLMetric<- | MLMetric Class Constructor
MLModel | MLModel and MLModelFunction Class Constructors
MLModelFunction | MLModel and MLModelFunction Class Constructors
ModelFrame | ModelFrame Class
ModelFrame.formula | ModelFrame Class
ModelFrame.matrix | ModelFrame Class
modelinfo | Display Model Information
models | Models
ModelSpecification | Model Specification
ModelSpecification.default | Model Specification
ModelSpecification.formula | Model Specification
ModelSpecification.matrix | Model Specification
ModelSpecification.ModelFrame | Model Specification
ModelSpecification.recipe | Model Specification
mse | Performance Metrics
msle | Performance Metrics

## -- N --

NaiveBayesModel | Naive Bayes Classifier Model
NegBinomialVariate | Discrete Variate Constructors
NNetModel | Neural Network Model
npv | Performance Metrics

## -- O --

OOBControl | Resampling Controls

## -- P --

ParameterGrid | Tuning Parameters Grid
ParameterGrid.list | Tuning Parameters Grid
ParameterGrid.param | Tuning Parameters Grid
ParameterGrid.parameters | Tuning Parameters Grid
ParsnipModel | Parsnip Model
PDAModel | Flexible and Penalized Discriminant Analysis Models
performance | Model Performance Metrics
performance.BinomialVariate | Model Performance Metrics
performance.ConfusionList | Model Performance Metrics
performance.ConfusionMatrix | Model Performance Metrics
performance.factor | Model Performance Metrics
performance.matrix | Model Performance Metrics
performance.MLModel | Model Performance Metrics
performance.numeric | Model Performance Metrics
performance.Resample | Model Performance Metrics
performance.Surv | Model Performance Metrics
performance.TrainingStep | Model Performance Metrics
performance_curve | Model Performance Curves
performance_curve.default | Model Performance Curves
performance_curve.Resample | Model Performance Curves
plot | Model Performance Plots
plot.Calibration | Model Performance Plots
plot.ConfusionList | Model Performance Plots
plot.ConfusionMatrix | Model Performance Plots
plot.LiftCurve | Model Performance Plots
plot.MLModel | Model Performance Plots
plot.PartialDependence | Model Performance Plots
plot.Performance | Model Performance Plots
plot.PerformanceCurve | Model Performance Plots
plot.Resample | Model Performance Plots
plot.TrainingStep | Model Performance Plots
plot.VariableImportance | Model Performance Plots
PLSModel | Partial Least Squares Model
PoissonVariate | Discrete Variate Constructors
POLRModel | Ordered Logistic or Probit Regression Model
ppr | Performance Metrics
ppv | Performance Metrics
precision | Performance Metrics
predict | Model Prediction
predict-method | Model Prediction
predict.MLModelFit | Model Prediction
print | Print MachineShop Objects
print.BinomialVariate | Print MachineShop Objects
print.Calibration | Print MachineShop Objects
print.DiscreteVariate | Print MachineShop Objects
print.ListOf | Print MachineShop Objects
print.MLControl | Print MachineShop Objects
print.MLMetric | Print MachineShop Objects
print.MLModel | Print MachineShop Objects
print.MLModelFunction | Print MachineShop Objects
print.ModelFrame | Print MachineShop Objects
print.ModelRecipe | Print MachineShop Objects
print.ModelSpecification | Print MachineShop Objects
print.Performance | Print MachineShop Objects
print.PerformanceCurve | Print MachineShop Objects
print.RecipeGrid | Print MachineShop Objects
print.Resample | Print MachineShop Objects
print.SurvMatrix | Print MachineShop Objects
print.SurvTimes | Print MachineShop Objects
print.TrainingStep | Print MachineShop Objects
print.VariableImportance | Print MachineShop Objects
pr_auc | Performance Metrics

## -- Q --

QDAModel | Quadratic Discriminant Analysis Model
quote | Quote Operator

## -- R --

r2 | Performance Metrics
RandomForestModel | Random Forest Model
RangerModel | Fast Random Forest Model
recall | Performance Metrics
recipe_roles | Set Recipe Roles
resample | Resample Estimation of Model Performance
resample.formula | Resample Estimation of Model Performance
resample.matrix | Resample Estimation of Model Performance
resample.MLModel | Resample Estimation of Model Performance
resample.MLModelFunction | Resample Estimation of Model Performance
resample.ModelFrame | Resample Estimation of Model Performance
resample.ModelSpecification | Resample Estimation of Model Performance
resample.recipe | Resample Estimation of Model Performance
response | Extract Response Variable
response.MLModelFit | Extract Response Variable
response.ModelFrame | Extract Response Variable
response.ModelSpecification | Extract Response Variable
response.recipe | Extract Response Variable
rfe | Recursive Feature Elimination
rfe.formula | Recursive Feature Elimination
rfe.matrix | Recursive Feature Elimination
rfe.MLModel | Recursive Feature Elimination
rfe.MLModelFunction | Recursive Feature Elimination
rfe.ModelFrame | Recursive Feature Elimination
rfe.ModelSpecification | Recursive Feature Elimination
rfe.recipe | Recursive Feature Elimination
RFSRCFastModel | Fast Random Forest (SRC) Model
RFSRCModel | Fast Random Forest (SRC) Model
rmse | Performance Metrics
rmsle | Performance Metrics
roc_auc | Performance Metrics
roc_index | Performance Metrics
role_binom | Set Recipe Roles
role_case | Set Recipe Roles
role_pred | Set Recipe Roles
role_surv | Set Recipe Roles
RPartModel | Recursive Partitioning and Regression Tree Models

## -- S --

SelectedInput | Selected Model Inputs
SelectedInput.formula | Selected Model Inputs
SelectedInput.list | Selected Model Inputs
SelectedInput.matrix | Selected Model Inputs
SelectedInput.ModelFrame | Selected Model Inputs
SelectedInput.ModelSpecification | Selected Model Inputs
SelectedInput.recipe | Selected Model Inputs
SelectedModel | Selected Model
SelectedModel.default | Selected Model
SelectedModel.list | Selected Model
SelectedModel.ModelSpecification | Selected Model
SelectedModelFrame | Selected Model Inputs
SelectedModelRecipe | Selected Model Inputs
SelectedModelSpecification | Selected Model Inputs
sensitivity | Performance Metrics
settings | MachineShop Settings
set_monitor | Training Parameters Monitoring Control
set_monitor.MLControl | Training Parameters Monitoring Control
set_monitor.MLOptimization | Training Parameters Monitoring Control
set_monitor.ModelSpecification | Training Parameters Monitoring Control
set_optim | Tuning Parameter Optimization
set_optim_bayes | Tuning Parameter Optimization
set_optim_bayes.ModelSpecification | Tuning Parameter Optimization
set_optim_bfgs | Tuning Parameter Optimization
set_optim_bfgs.ModelSpecification | Tuning Parameter Optimization
set_optim_grid | Tuning Parameter Optimization
set_optim_grid.ModelSpecification | Tuning Parameter Optimization
set_optim_grid.TrainingParams | Tuning Parameter Optimization
set_optim_grid.TunedInput | Tuning Parameter Optimization
set_optim_grid.TunedModel | Tuning Parameter Optimization
set_optim_method | Tuning Parameter Optimization
set_optim_method.ModelSpecification | Tuning Parameter Optimization
set_optim_pso | Tuning Parameter Optimization
set_optim_pso.ModelSpecification | Tuning Parameter Optimization
set_optim_sann | Tuning Parameter Optimization
set_optim_sann.ModelSpecification | Tuning Parameter Optimization
set_predict | Resampling Prediction Control
set_strata | Resampling Stratification Control
specificity | Performance Metrics
SplitControl | Resampling Controls
StackedModel | Stacked Regression Model
step_kmeans | K-Means Clustering Variable Reduction
step_kmedoids | K-Medoids Clustering Variable Selection
step_lincomp | Linear Components Variable Reduction
step_sbf | Variable Selection by Filtering
step_spca | Sparse Principal Components Analysis Variable Reduction
summary | Model Performance Summaries
summary.ConfusionList | Model Performance Summaries
summary.ConfusionMatrix | Model Performance Summaries
summary.MLModel | Model Performance Summaries
summary.MLModelFit | Model Performance Summaries
summary.Performance | Model Performance Summaries
summary.PerformanceCurve | Model Performance Summaries
summary.Resample | Model Performance Summaries
summary.TrainingStep | Model Performance Summaries
SuperModel | Super Learner Model
SurvEvents | SurvMatrix Class Constructors
SurvMatrix | SurvMatrix Class Constructors
SurvProbs | SurvMatrix Class Constructors
SurvRegModel | Parametric Survival Model
SurvRegStepAICModel | Parametric Survival Model
SVMANOVAModel | Support Vector Machine Models
SVMBesselModel | Support Vector Machine Models
SVMLaplaceModel | Support Vector Machine Models
SVMLinearModel | Support Vector Machine Models
SVMModel | Support Vector Machine Models
SVMPolyModel | Support Vector Machine Models
SVMRadialModel | Support Vector Machine Models
SVMSplineModel | Support Vector Machine Models
SVMTanhModel | Support Vector Machine Models

## -- T --

t.test | Paired t-Tests for Model Comparisons
t.test.PerformanceDiff | Paired t-Tests for Model Comparisons
tidy.step_kmeans | K-Means Clustering Variable Reduction
tidy.step_lincomp | Linear Components Variable Reduction
tidy.step_sbf | Variable Selection by Filtering
tnr | Performance Metrics
tpr | Performance Metrics
TrainControl | Resampling Controls
TreeModel | Classification and Regression Tree Models
tunable.step_kmeans | K-Means Clustering Variable Reduction
tunable.step_kmedoids | K-Medoids Clustering Variable Selection
tunable.step_lincomp | Linear Components Variable Reduction
tunable.step_spca | Sparse Principal Components Analysis Variable Reduction
TunedInput | Tuned Model Inputs
TunedInput.recipe | Tuned Model Inputs
TunedModel | Tuned Model
TunedModelRecipe | Tuned Model Inputs
TuningGrid | Tuning Grid Control

## -- U --

unMLModelFit | Revert an MLModelFit Object

## -- V --

varimp | Variable Importance

## -- W --

weighted_kappa2 | Performance Metrics

## -- X --

XGBDARTModel | Extreme Gradient Boosting Models
XGBLinearModel | Extreme Gradient Boosting Models
XGBModel | Extreme Gradient Boosting Models
XGBTreeModel | Extreme Gradient Boosting Models

## -- misc --

+-method | Combine MachineShop Objects
. | Quote Operator
[-method | Extract Elements of an Object
[.BinomialVariate | Extract Elements of an Object