A B C D E F G I K L M N O P Q R S T V W X misc
| MachineShop-package | MachineShop: Machine Learning Models and Tools |
| accuracy | Performance Metrics |
| AdaBagModel | Bagging with Classification Trees |
| AdaBoostModel | Boosting with Classification Trees |
| auc | Performance Metrics |
| BARTMachineModel | Bayesian Additive Regression Trees Model |
| BARTModel | Bayesian Additive Regression Trees Model |
| BlackBoostModel | Gradient Boosting with Regression Trees |
| BootControl | Resampling Controls |
| BootOptimismControl | Resampling Controls |
| brier | Performance Metrics |
| C50Model | C5.0 Decision Trees and Rule-Based Model |
| Calibration | Model Calibration |
| calibration | Model Calibration |
| CForestModel | Conditional Random Forest Model |
| cindex | Performance Metrics |
| Confusion | Confusion Matrix |
| confusion | Confusion Matrix |
| ConfusionMatrix | Confusion Matrix |
| CoxModel | Proportional Hazards Regression Model |
| CoxStepAICModel | Proportional Hazards Regression Model |
| cross_entropy | Performance Metrics |
| Curves | Performance Curves |
| CVControl | Resampling Controls |
| dependence | Partial Dependence |
| diff | Model Performance Differences |
| diff.MLModelTune | Model Performance Differences |
| diff.Performance | Model Performance Differences |
| diff.Resamples | Model Performance Differences |
| EarthModel | Multivariate Adaptive Regression Splines Model |
| expand.model | Model Expansion Over a Grid of Tuning Parameters |
| extract | Extract Parts of an Object |
| FDAModel | Flexible and Penalized Discriminant Analysis Models |
| fit | Model Fitting |
| fit.formula | Model Fitting |
| fit.matrix | Model Fitting |
| fit.ModelFrame | Model Fitting |
| fit.recipe | Model Fitting |
| fnr | Performance Metrics |
| fpr | Performance Metrics |
| f_score | Performance Metrics |
| 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 |
| Grid | Tuning Grid Control |
| ICHomes | Iowa City Home Sales Dataset |
| kappa2 | Performance Metrics |
| KNNModel | Weighted k-Nearest Neighbor Model |
| LARSModel | Least Angle Regression, Lasso and Infinitesimal Forward Stagewise Models |
| LDAModel | Linear Discriminant Analysis Model |
| Lift | Model Lift |
| lift | Model Lift |
| LMModel | Linear Models |
| 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 Class Constructor |
| ModelFrame | ModelFrame Class |
| ModelFrame.formula | ModelFrame Class |
| ModelFrame.matrix | ModelFrame Class |
| modelinfo | Display Model Information |
| models | Display Model Information |
| mse | Performance Metrics |
| msle | Performance Metrics |
| NaiveBayesModel | Naive Bayes Classifier Model |
| NNetModel | Neural Network Model |
| npv | Performance Metrics |
| OOBControl | Resampling Controls |
| PDAModel | Flexible and Penalized Discriminant Analysis Models |
| performance | Model Performance Metrics |
| performance.Confusion | Model Performance Metrics |
| performance.ConfusionMatrix | Model Performance Metrics |
| performance.factor | Model Performance Metrics |
| performance.matrix | Model Performance Metrics |
| performance.numeric | Model Performance Metrics |
| performance.Resamples | Model Performance Metrics |
| performance.Surv | Model Performance Metrics |
| performance_curve | Performance Curves |
| performance_curve.default | Performance Curves |
| performance_curve.Resamples | Performance Curves |
| plot | Model Performance Plots |
| plot.Calibration | Model Performance Plots |
| plot.Confusion | Model Performance Plots |
| plot.ConfusionMatrix | Model Performance Plots |
| plot.Curves | Model Performance Plots |
| plot.Lift | Model Performance Plots |
| plot.MLModelTune | Model Performance Plots |
| plot.PartialDependence | Model Performance Plots |
| plot.Performance | Model Performance Plots |
| plot.Resamples | Model Performance Plots |
| plot.VarImp | Model Performance Plots |
| PLSModel | Partial Least Squares Model |
| POLRModel | Ordered Logistic or Probit Regression Model |
| ppv | Performance Metrics |
| precision | Performance Metrics |
| predict | Model Prediction |
| predict.MLModelFit | Model Prediction |
| pr_auc | Performance Metrics |
| QDAModel | Quadratic Discriminant Analysis Model |
| r2 | Performance Metrics |
| RandomForestModel | Random Forest Model |
| RangerModel | Fast Random Forest Model |
| recall | Performance Metrics |
| resample | Resample Estimation of Model Performance |
| resample.formula | Resample Estimation of Model Performance |
| resample.matrix | Resample Estimation of Model Performance |
| resample.ModelFrame | Resample Estimation of Model Performance |
| resample.recipe | Resample Estimation of Model Performance |
| Resamples | Resample Estimation of Model Performance |
| response | Extract Response Variable |
| response.MLModelFit | Extract Response Variable |
| response.ModelFrame | Extract Response Variable |
| response.recipe | Extract Response Variable |
| rmse | Performance Metrics |
| rmsle | Performance Metrics |
| roc_auc | Performance Metrics |
| roc_index | Performance Metrics |
| RPartModel | Recursive Partitioning and Regression Tree Models |
| rpp | Performance Metrics |
| SelectedModel | Selected Model |
| sensitivity | Performance Metrics |
| specificity | Performance Metrics |
| SplitControl | Resampling Controls |
| StackedModel | Stacked Regression Model |
| summary | Model Performance Summary |
| summary.Confusion | Model Performance Summary |
| summary.ConfusionMatrix | Model Performance Summary |
| summary.Curves | Model Performance Summary |
| summary.MLModelTune | Model Performance Summary |
| summary.Performance | Model Performance Summary |
| summary.Resamples | Model Performance Summary |
| SuperModel | Super Learner Model |
| SurvEvents | SurvMatrix Class Constructor |
| SurvMatrix | SurvMatrix Class Constructor |
| SurvProbs | SurvMatrix Class Constructor |
| 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.test | Paired t-Tests for Model Comparisons |
| t.test.PerformanceDiff | Paired t-Tests for Model Comparisons |
| tnr | Performance Metrics |
| tpr | Performance Metrics |
| TrainControl | Resampling Controls |
| TreeModel | Classification and Regression Tree Models |
| tune | Model Tuning and Selection |
| tune.formula | Model Tuning and Selection |
| tune.matrix | Model Tuning and Selection |
| tune.ModelFrame | Model Tuning and Selection |
| tune.recipe | Model Tuning and Selection |
| TunedModel | Tuned Model |
| varimp | Variable Importance |
| weighted_kappa2 | Performance Metrics |
| XGBDARTModel | Extreme Gradient Boosting Models |
| XGBLinearModel | Extreme Gradient Boosting Models |
| XGBModel | Extreme Gradient Boosting Models |
| XGBTreeModel | Extreme Gradient Boosting Models |
| . | Quote Operator |
| [-method | Extract Parts of an Object |
| [.SurvMatrix | Extract Parts of an Object |