Man pages for MachineShop
Machine Learning Models and Tools

AdaBagModelBagging with Classification Trees
AdaBoostModelBoosting with Classification Trees
as.MLInputCoerce to an MLInput
as.MLModelCoerce to an MLModel
BARTMachineModelBayesian Additive Regression Trees Model
BARTModelBayesian Additive Regression Trees Model
BlackBoostModelGradient Boosting with Regression Trees
C50ModelC5.0 Decision Trees and Rule-Based Model
calibrationModel Calibration
case_weightsExtract Case Weights
CForestModelConditional Random Forest Model
combine-methodsCombine MachineShop Objects
confusionConfusion Matrix
CoxModelProportional Hazards Regression Model
dependencePartial Dependence
deprecatedDeprecated Functions
diff-methodsModel Performance Differences
DiscreteVariateDiscrete Variate Constructors
EarthModelMultivariate Adaptive Regression Splines Model
expand_modelModel Expansion Over Tuning Parameters
expand_modelgrid-methodsModel Tuning Grid Expansion
expand_paramsModel Parameters Expansion
expand_stepsRecipe Step Parameters Expansion
extract-methodsExtract Elements of an Object
FDAModelFlexible and Penalized Discriminant Analysis Models
fit-methodsModel Fitting
GAMBoostModelGradient Boosting with Additive Models
GBMModelGeneralized Boosted Regression Model
GLMBoostModelGradient Boosting with Linear Models
GLMModelGeneralized Linear Model
GLMNetModelGLM Lasso or Elasticnet Model
ICHomesIowa City Home Sales Dataset
inputsModel Inputs
KNNModelWeighted k-Nearest Neighbor Model
LARSModelLeast Angle Regression, Lasso and Infinitesimal Forward...
LDAModelLinear Discriminant Analysis Model
liftModel Lift Curves
LMModelLinear Models
MachineShop-packageMachineShop: Machine Learning Models and Tools
MDAModelMixture Discriminant Analysis Model
metricinfoDisplay Performance Metric Information
metricsPerformance Metrics
MLControlResampling Controls
MLMetricMLMetric Class Constructor
MLModelMLModel and MLModelFunction Class Constructors
ModelFrame-methodsModelFrame Class
modelinfoDisplay Model Information
ModelSpecification-methodsModel Specification
NaiveBayesModelNaive Bayes Classifier Model
NNetModelNeural Network Model
ParameterGridTuning Parameters Grid
ParsnipModelParsnip Model
performanceModel Performance Metrics
performance_curveModel Performance Curves
plot-methodsModel Performance Plots
PLSModelPartial Least Squares Model
POLRModelOrdered Logistic or Probit Regression Model
predictModel Prediction
print-methodsPrint MachineShop Objects
QDAModelQuadratic Discriminant Analysis Model
quoteQuote Operator
RandomForestModelRandom Forest Model
RangerModelFast Random Forest Model
recipe_rolesSet Recipe Roles
reexportsObjects exported from other packages
resample-methodsResample Estimation of Model Performance
response-methodsExtract Response Variable
rfe-methodsRecursive Feature Elimination
RFSRCModelFast Random Forest (SRC) Model
RPartModelRecursive Partitioning and Regression Tree Models
SelectedInputSelected Model Inputs
SelectedModelSelected Model
set_monitor-methodsTraining Parameters Monitoring Control
set_optim-methodsTuning Parameter Optimization
set_predictResampling Prediction Control
set_strataResampling Stratification Control
settingsMachineShop Settings
StackedModelStacked Regression Model
step_kmeansK-Means Clustering Variable Reduction
step_kmedoidsK-Medoids Clustering Variable Selection
step_lincompLinear Components Variable Reduction
step_sbfVariable Selection by Filtering
step_spcaSparse Principal Components Analysis Variable Reduction
summary-methodsModel Performance Summaries
SuperModelSuper Learner Model
SurvMatrixSurvMatrix Class Constructors
SurvRegModelParametric Survival Model
SVMModelSupport Vector Machine Models
TreeModelClassification and Regression Tree Models
t.testPaired t-Tests for Model Comparisons
TunedInputTuned Model Inputs
TunedModelTuned Model
TuningGridTuning Grid Control
unMLModelFitRevert an MLModelFit Object
varimpVariable Importance
XGBModelExtreme Gradient Boosting Models
MachineShop documentation built on June 3, 2022, 9:05 a.m.