Man pages for mlr3
Machine Learning in R - Next Generation
| as_benchmark_result | Convert to BenchmarkResult |
| as_data_backend | Create a Data Backend |
| as_learner | Convert to a Learner |
| as_measure | Convert to a Measure |
| as_prediction | Convert to a Prediction |
| as_prediction_classif | Convert to a Classification Prediction |
| as_prediction_data | PredictionData |
| as_prediction_regr | Convert to a Regression Prediction |
| as_resample_result | Convert to ResampleResult |
| as_resampling | Convert to a Resampling |
| as_result_data | Convert to ResultData |
| assert_empty_ellipsis | Assert Empty Ellipsis |
| assert_resample_callback | Assertions for Callbacks |
| as_task | Convert to a Task |
| as_task_classif | Convert to a Classification Task |
| as_task_regr | Convert to a Regression Task |
| as_task_unsupervised | Convert to an Unsupervised Task |
| auto_convert | Column Auto-Converter |
| benchmark | Benchmark Multiple Learners on Multiple Tasks |
| benchmark_grid | Generate a Benchmark Grid Design |
| BenchmarkResult | Container for Benchmarking Results |
| california_housing | Median House Value in California |
| callback_resample | Create Evaluation Callback |
| CallbackResample | Resample Callback |
| col_info | Column Information for Backend |
| ContextResample | Resample Context |
| convert_task | Convert a Task from One Type to Another |
| DataBackend | DataBackend |
| DataBackendDataTable | DataBackend for data.table |
| default_fallback | Create a Fallback Learner |
| default_measures | Get the Default Measure |
| deprecated_binding | Create an Active Binding that Generates a Deprecation Warning |
| HotstartStack | Stack for Hot Start Learners |
| install_pkgs | Install (Missing) Packages |
| Learner | Learner Class |
| LearnerClassif | Classification Learner |
| LearnerRegr | Regression Learner |
| marshaling | (Un)marshal a Learner |
| Measure | Measure Class |
| MeasureClassif | Classification Measure |
| MeasureRegr | Regression Measure |
| MeasureSimilarity | Similarity Measure |
| mlr3.holdout_task | Callback Holdout Task |
| mlr3.model_extractor | Model Extractor Callback |
| mlr3-package | mlr3: Machine Learning in R - Next Generation |
| mlr_assertions | Assertion for mlr3 Objects |
| mlr_learners | Dictionary of Learners |
| mlr_learners_classif.debug | Classification Learner for Debugging |
| mlr_learners_classif.featureless | Featureless Classification Learner |
| mlr_learners_classif.rpart | Classification Tree Learner |
| mlr_learners_regr.debug | Regression Learner for Debugging |
| mlr_learners_regr.featureless | Featureless Regression Learner |
| mlr_learners_regr.rpart | Regression Tree Learner |
| mlr_measures | Dictionary of Performance Measures |
| mlr_measures_aic | Akaike Information Criterion Measure |
| mlr_measures_bic | Bayesian Information Criterion Measure |
| mlr_measures_classif.acc | Classification Accuracy |
| mlr_measures_classif.auc | Area Under the ROC Curve |
| mlr_measures_classif.bacc | Balanced Accuracy |
| mlr_measures_classif.bbrier | Binary Brier Score |
| mlr_measures_classif.ce | Classification Error |
| mlr_measures_classif.costs | Cost-sensitive Classification Measure |
| mlr_measures_classif.dor | Diagnostic Odds Ratio |
| mlr_measures_classif.fbeta | F-beta Score |
| mlr_measures_classif.fdr | False Discovery Rate |
| mlr_measures_classif.fn | False Negatives |
| mlr_measures_classif.fnr | False Negative Rate |
| mlr_measures_classif.fomr | False Omission Rate |
| mlr_measures_classif.fp | False Positives |
| mlr_measures_classif.fpr | False Positive Rate |
| mlr_measures_classif.logloss | Log Loss |
| mlr_measures_classif.mauc_au1p | Multiclass AUC Scores |
| mlr_measures_classif.mauc_au1u | Multiclass AUC Scores |
| mlr_measures_classif.mauc_aunp | Multiclass AUC Scores |
| mlr_measures_classif.mauc_aunu | Multiclass AUC Scores |
| mlr_measures_classif.mauc_mu | Multiclass AUC Scores |
| mlr_measures_classif.mbrier | Multiclass Brier Score |
| mlr_measures_classif.mcc | Matthews Correlation Coefficient |
| mlr_measures_classif.npv | Negative Predictive Value |
| mlr_measures_classif.ppv | Positive Predictive Value |
| mlr_measures_classif.prauc | Area Under the Precision-Recall Curve |
| mlr_measures_classif.precision | Positive Predictive Value |
| mlr_measures_classif.recall | True Positive Rate |
| mlr_measures_classif.sensitivity | True Positive Rate |
| mlr_measures_classif.specificity | True Negative Rate |
| mlr_measures_classif.tn | True Negatives |
| mlr_measures_classif.tnr | True Negative Rate |
| mlr_measures_classif.tp | True Positives |
| mlr_measures_classif.tpr | True Positive Rate |
| mlr_measures_debug_classif | Debug Measure for Classification |
| mlr_measures_elapsed_time | Elapsed Time Measure |
| mlr_measures_internal_valid_score | Measure Internal Validation Score |
| mlr_measures_oob_error | Out-of-bag Error Measure |
| mlr_measures_regr.bias | Bias |
| mlr_measures_regr.ktau | Kendall's tau |
| mlr_measures_regr.mae | Mean Absolute Error |
| mlr_measures_regr.mape | Mean Absolute Percent Error |
| mlr_measures_regr.maxae | Max Absolute Error |
| mlr_measures_regr.medae | Median Absolute Error |
| mlr_measures_regr.medse | Median Squared Error |
| mlr_measures_regr.mse | Mean Squared Error |
| mlr_measures_regr.msle | Mean Squared Log Error |
| mlr_measures_regr.pbias | Percent Bias |
| mlr_measures_regr.pinball | Average Pinball Loss |
| mlr_measures_regr.rmse | Root Mean Squared Error |
| mlr_measures_regr.rmsle | Root Mean Squared Log Error |
| mlr_measures_regr.rqr | R-Squared for Quantile Regression |
| mlr_measures_regr.rsq | R-Squared |
| mlr_measures_regr.sae | Sum of Absolute Errors |
| mlr_measures_regr.smape | Symmetric Mean Absolute Percent Error |
| mlr_measures_regr.srho | Spearman's rho |
| mlr_measures_regr.sse | Sum of Squared Errors |
| mlr_measures_selected_features | Selected Features Measure |
| mlr_measures_sim.jaccard | Jaccard Similarity Index |
| mlr_measures_sim.phi | Phi Coefficient Similarity |
| mlr_reflections | Reflections for mlr3 |
| mlr_resamplings | Dictionary of Resampling Strategies |
| mlr_resamplings_bootstrap | Bootstrap Resampling |
| mlr_resamplings_custom | Custom Resampling |
| mlr_resamplings_custom_cv | Custom Cross-Validation |
| mlr_resamplings_cv | Cross-Validation Resampling |
| mlr_resamplings_holdout | Holdout Resampling |
| mlr_resamplings_insample | Insample Resampling |
| mlr_resamplings_loo | Leave-One-Out Cross-Validation |
| mlr_resamplings_repeated_cv | Repeated Cross-Validation Resampling |
| mlr_resamplings_subsampling | Subsampling Resampling |
| mlr_sugar | Syntactic Sugar for Object Construction |
| mlr_task_generators | Dictionary of Task Generators |
| mlr_task_generators_2dnormals | 2D Normals Classification Task Generator |
| mlr_task_generators_cassini | Cassini Classification Task Generator |
| mlr_task_generators_circle | Circle Classification Task Generator |
| mlr_task_generators_friedman1 | Friedman1 Regression Task Generator |
| mlr_task_generators_moons | Moons Classification Task Generator |
| mlr_task_generators_peak | Peak Regression Task Generator |
| mlr_task_generators_simplex | Simplex Classification Task Generator |
| mlr_task_generators_smiley | Smiley Classification Task Generator |
| mlr_task_generators_spirals | Spiral Classification Task Generator |
| mlr_task_generators_xor | XOR Classification Task Generator |
| mlr_tasks | Dictionary of Tasks |
| mlr_tasks_breast_cancer | Wisconsin Breast Cancer Classification Task |
| mlr_tasks_german_credit | German Credit Classification Task |
| mlr_tasks_iris | Iris Classification Task |
| mlr_tasks_mtcars | Motor Trend Regression Task |
| mlr_tasks_penguins | Palmer Penguins Data Set |
| mlr_tasks_pima | Pima Indian Diabetes Classification Task |
| mlr_tasks_sonar | Sonar Classification Task |
| mlr_tasks_spam | Spam Classification Task |
| mlr_tasks_wine | Wine Classification Task |
| mlr_tasks_zoo | Zoo Classification Task |
| mlr_test_helpers | Documentation of mlr3 test helpers |
| partition | Manually Partition into Training, Test and Validation Set |
| Prediction | Abstract Prediction Object |
| PredictionClassif | Prediction Object for Classification |
| PredictionData | Convert to PredictionData |
| PredictionRegr | Prediction Object for Regression |
| predict.Learner | Predict Method for Learners |
| print.roc_measures | Print ROC Measures |
| reexports | Objects exported from other packages |
| resample | Resample a Learner on a Task |
| ResampleResult | Container for Results of 'resample()' |
| Resampling | Resampling Class |
| ResultData | ResultData |
| score_roc_measures | Calculate ROC Measures |
| set_threads | Set the Number of Threads |
| Task | Task Class |
| task_check_col_roles | Check Column Roles |
| TaskClassif | Classification Task |
| TaskGenerator | TaskGenerator Class |
| TaskRegr | Regression Task |
| TaskSupervised | Supervised Task |
| TaskUnsupervised | Unsupervised Task |
| uhash | Obtain specific uhashes from a BenchmarkResult |
| warn_deprecated | Give a Warning about a Deprecated Function, Argument, or... |
