Man pages for MachineShop
Machine Learning Models and Tools
| AdaBagModel | Bagging with Classification Trees |
| AdaBoostModel | Boosting with Classification Trees |
| as.data.frame | Coerce to a Data Frame |
| as.MLInput | Coerce to an MLInput |
| as.MLModel | Coerce to an MLModel |
| BARTMachineModel | Bayesian Additive Regression Trees Model |
| BARTModel | Bayesian Additive Regression Trees Model |
| BlackBoostModel | Gradient Boosting with Regression Trees |
| C50Model | C5.0 Decision Trees and Rule-Based Model |
| calibration | Model Calibration |
| case_weights | Extract Case Weights |
| CForestModel | Conditional Random Forest Model |
| combine-methods | Combine MachineShop Objects |
| confusion | Confusion Matrix |
| CoxModel | Proportional Hazards Regression Model |
| dependence | Partial Dependence |
| diff-methods | Model Performance Differences |
| DiscreteVariate | Discrete Variate Constructors |
| EarthModel | Multivariate Adaptive Regression Splines Model |
| expand_model | Model Expansion Over Tuning Parameters |
| expand_modelgrid-methods | Model Tuning Grid Expansion |
| expand_params | Model Parameters Expansion |
| expand_steps | Recipe Step Parameters Expansion |
| extract-methods | Extract Elements of an Object |
| FDAModel | Flexible and Penalized Discriminant Analysis Models |
| fit-methods | Model Fitting |
| GAMBoostModel | Gradient Boosting with Additive Models |
| GBMModel | Generalized Boosted Regression Model |
| GLMBoostModel | Gradient Boosting with Linear Models |
| GLMModel | Generalized Linear Model |
| GLMNetModel | GLM Lasso or Elasticnet Model |
| ICHomes | Iowa City Home Sales Dataset |
| inputs | Model Inputs |
| KNNModel | Weighted k-Nearest Neighbor Model |
| LARSModel | Least Angle Regression, Lasso and Infinitesimal Forward... |
| LDAModel | Linear Discriminant Analysis Model |
| lift | Model Lift Curves |
| LMModel | Linear Models |
| MachineShop-package | MachineShop: Machine Learning Models and Tools |
| MDAModel | Mixture Discriminant Analysis Model |
| metricinfo | Display Performance Metric Information |
| metrics | Performance Metrics |
| MLControl | Resampling Controls |
| MLMetric | MLMetric Class Constructor |
| MLModel | MLModel and MLModelFunction Class Constructors |
| ModelFrame-methods | ModelFrame Class |
| modelinfo | Display Model Information |
| models | Models |
| ModelSpecification-methods | Model Specification |
| NaiveBayesModel | Naive Bayes Classifier Model |
| NNetModel | Neural Network Model |
| ParameterGrid | Tuning Parameters Grid |
| ParsnipModel | Parsnip Model |
| performance | Model Performance Metrics |
| performance_curve | Model Performance Curves |
| plot-methods | Model Performance Plots |
| PLSModel | Partial Least Squares Model |
| POLRModel | Ordered Logistic or Probit Regression Model |
| predict | Model Prediction |
| print-methods | Print MachineShop Objects |
| QDAModel | Quadratic Discriminant Analysis Model |
| quote | Quote Operator |
| RandomForestModel | Random Forest Model |
| RangerModel | Fast Random Forest Model |
| recipe_roles | Set Recipe Roles |
| reexports | Objects exported from other packages |
| resample-methods | Resample Estimation of Model Performance |
| response-methods | Extract Response Variable |
| rfe-methods | Recursive Feature Elimination |
| RFSRCModel | Fast Random Forest (SRC) Model |
| RPartModel | Recursive Partitioning and Regression Tree Models |
| SelectedInput | Selected Model Inputs |
| SelectedModel | Selected Model |
| set_monitor-methods | Training Parameters Monitoring Control |
| set_optim-methods | Tuning Parameter Optimization |
| set_predict | Resampling Prediction Control |
| set_strata | Resampling Stratification Control |
| settings | MachineShop Settings |
| 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-methods | Model Performance Summaries |
| SuperModel | Super Learner Model |
| SurvMatrix | SurvMatrix Class Constructors |
| SurvRegModel | Parametric Survival Model |
| SVMModel | Support Vector Machine Models |
| TreeModel | Classification and Regression Tree Models |
| t.test | Paired t-Tests for Model Comparisons |
| TunedInput | Tuned Model Inputs |
| TunedModel | Tuned Model |
| TuningGrid | Tuning Grid Control |
| unMLModelFit | Revert an MLModelFit Object |
| varimp | Variable Importance |
| XGBModel | Extreme Gradient Boosting Models |
