Man pages for Najah-lshanableh/R-data-mining2
Machine Learning in R
| addRRMeasure | Compute new measures for existing ResampleResult |
| Aggregation | Aggregation object. |
| aggregations | Aggregation methods. |
| agri.task | European Union Agricultural Workforces clustering task. |
| analyzeFeatSelResult | Show and visualize the steps of feature selection. |
| asROCRPrediction | Converts predictions to a format package ROCR can handle. |
| batchmark | Run machine learning benchmarks as distributed experiments. |
| bc.task | Wisconsin Breast Cancer classification task. |
| benchmark | Benchmark experiment for multiple learners and tasks. |
| BenchmarkResult | BenchmarkResult object. |
| bh.task | Boston Housing regression task. |
| calculateConfusionMatrix | Confusion matrix. |
| calculateROCMeasures | Calculate receiver operator measures. |
| capLargeValues | Convert large/infinite numeric values in a data.frame or... |
| classif.featureless | Featureless classification learner. |
| configureMlr | Configures the behavior of the package. |
| ConfusionMatrix | Confusion matrix |
| convertBMRToRankMatrix | Convert BenchmarkResult to a rank-matrix. |
| convertMLBenchObjToTask | Convert a machine learning benchmark / demo object from... |
| costiris.task | Iris cost-sensitive classification task. |
| createDummyFeatures | Generate dummy variables for factor features. |
| crossover | Crossover. |
| downsample | Downsample (subsample) a task or a data.frame. |
| dropFeatures | Drop some features of task. |
| estimateRelativeOverfitting | Estimate relative overfitting. |
| estimateResidualVariance | Estimate the residual variance. |
| FailureModel | Failure model. |
| FeatSelControl | Create control structures for feature selection. |
| FeatSelResult | Result of feature selection. |
| filterFeatures | Filter features by thresholding filter values. |
| friedmanPostHocTestBMR | Perform a posthoc Friedman-Nemenyi test. |
| friedmanTestBMR | Perform overall Friedman test for a BenchmarkResult. |
| generateCalibrationData | Generate classifier calibration data. |
| generateCritDifferencesData | Generate data for critical-differences plot. |
| generateFeatureImportanceData | Generate feature importance. |
| generateFilterValuesData | Calculates feature filter values. |
| generateFunctionalANOVAData | Generate a functional ANOVA decomposition |
| generateHyperParsEffectData | Generate hyperparameter effect data. |
| generateLearningCurveData | Generates a learning curve. |
| generatePartialDependenceData | Generate partial dependence. |
| generateThreshVsPerfData | Generate threshold vs. performance(s) for 2-class... |
| getBMRAggrPerformances | Extract the aggregated performance values from a benchmark... |
| getBMRFeatSelResults | Extract the feature selection results from a benchmark... |
| getBMRFilteredFeatures | Extract the feature selection results from a benchmark... |
| getBMRLearnerIds | Return learner ids used in benchmark. |
| getBMRLearners | Return learners used in benchmark. |
| getBMRLearnerShortNames | Return learner short.names used in benchmark. |
| getBMRMeasureIds | Return measures IDs used in benchmark. |
| getBMRMeasures | Return measures used in benchmark. |
| getBMRModels | Extract all models from benchmark result. |
| getBMRPerformances | Extract the test performance values from a benchmark result. |
| getBMRPredictions | Extract the predictions from a benchmark result. |
| getBMRTaskDescriptions | Extract all task descriptions from benchmark result... |
| getBMRTaskDescs | Extract all task descriptions from benchmark result. |
| getBMRTaskIds | Return task ids used in benchmark. |
| getBMRTuneResults | Extract the tuning results from a benchmark result. |
| getCaretParamSet | Get tuning parameters from a learner of the caret R-package. |
| getClassWeightParam | Get the class weight parameter of a learner. |
| getConfMatrix | Confusion matrix. |
| getDefaultMeasure | Get default measure. |
| getFailureModelDump | Return the error dump of FailureModel. |
| getFailureModelMsg | Return error message of FailureModel. |
| getFeatSelResult | Returns the selected feature set and optimization path after... |
| getFeatureImportance | Calculates feature importance values for trained models. |
| getFeatureImportanceLearner | Calculates feature importance values for a given learner. |
| getFilteredFeatures | Returns the filtered features. |
| getFilterValues | Calculates feature filter values. |
| getHomogeneousEnsembleModels | Deprecated, use 'getLearnerModel' instead. |
| getHyperPars | Get current parameter settings for a learner. |
| getLearnerId | Get the ID of the learner. |
| getLearnerModel | Get underlying R model of learner integrated into mlr. |
| getLearnerPackages | Get the required R packages of the learner. |
| getLearnerParamSet | Get the parameter set of the learner. |
| getLearnerParVals | Get the parameter values of the learner. |
| getLearnerPredictType | Get the predict type of the learner. |
| getLearnerShortName | Get the short name of the learner. |
| getLearnerType | Get the type of the learner. |
| getMlrOptions | Returns a list of mlr's options. |
| getMultilabelBinaryPerformances | Retrieve binary classification measures for multilabel... |
| getNestedTuneResultsOptPathDf | Get the 'opt.path's from each tuning step from the outer... |
| getNestedTuneResultsX | Get the tuned hyperparameter settings from a nested tuning. |
| getOOBPreds | Extracts out-of-bag predictions from trained models. |
| getOOBPredsLearner | Provides out-of-bag predictions for a given model and the... |
| getParamSet | Get a description of all possible parameter settings for a... |
| getPredictionDump | Return the error dump of a failed Prediction. |
| getPredictionProbabilities | Get probabilities for some classes. |
| getPredictionResponse | Get response / truth from prediction object. |
| getPredictionTaskDesc | Get summarizing task description from prediction. |
| getProbabilities | Deprecated, use 'getPredictionProbabilities' instead. |
| getRRDump | Return the error dump of ResampleResult. |
| getRRPredictionList | Get list of predictions for train and test set of each single... |
| getRRPredictions | Get predictions from resample results. |
| getRRTaskDesc | Get task description from resample results (DEPRECATED). |
| getRRTaskDescription | Get task description from resample results (DEPRECATED). |
| getStackedBaseLearnerPredictions | Returns the predictions for each base learner. |
| getTaskClassLevels | Get the class levels for classification and multilabel tasks. |
| getTaskCosts | Extract costs in task. |
| getTaskData | Extract data in task. |
| getTaskDesc | Get a summarizing task description. |
| getTaskDescription | Deprecated, use 'getTaskDesc' instead. |
| getTaskFeatureNames | Get feature names of task. |
| getTaskFormula | Get formula of a task. |
| getTaskId | Get the id of the task. |
| getTaskNFeats | Get number of features in task. |
| getTaskSize | Get number of observations in task. |
| getTaskTargetNames | Get the name(s) of the target column(s). |
| getTaskTargets | Get target data of task. |
| getTaskType | Get the type of the task. |
| getTuneResult | Returns the optimal hyperparameters and optimization path... |
| hasProperties | Deprecated, use 'hasLearnerProperties' instead. |
| helpLearner | Access help page of learner functions. |
| helpLearnerParam | Get specific help for a learner's parameters. |
| imputations | Built-in imputation methods. |
| impute | Impute and re-impute data |
| iris.task | Iris classification task. |
| isFailureModel | Is the model a FailureModel? |
| joinClassLevels | Join some class existing levels to new, larger class levels... |
| learnerArgsToControl | Convert arguments to control structure. |
| LearnerProperties | Query properties of learners. |
| learners | List of supported learning algorithms. |
| listFilterMethods | List filter methods. |
| listLearnerProperties | List the supported learner properties |
| listLearners | Find matching learning algorithms. |
| listMeasureProperties | List the supported measure properties. |
| listMeasures | Find matching measures. |
| listTaskTypes | List the supported task types in mlr |
| lung.task | NCCTG Lung Cancer survival task. |
| makeAggregation | Specify your own aggregation of measures. |
| makeBaggingWrapper | Fuse learner with the bagging technique. |
| makeClassificationViaRegressionWrapper | Classification via regression wrapper. |
| makeConstantClassWrapper | Wraps a classification learner to support problems where the... |
| makeCostMeasure | Creates a measure for non-standard misclassification costs. |
| makeCostSensClassifWrapper | Wraps a classification learner for use in cost-sensitive... |
| makeCostSensRegrWrapper | Wraps a regression learner for use in cost-sensitive... |
| makeCostSensWeightedPairsWrapper | Wraps a classifier for cost-sensitive learning to produce a... |
| makeCustomResampledMeasure | Construct your own resampled performance measure. |
| makeDownsampleWrapper | Fuse learner with simple downsampling (subsampling). |
| makeDummyFeaturesWrapper | Fuse learner with dummy feature creator. |
| makeFeatSelWrapper | Fuse learner with feature selection. |
| makeFilter | Create a feature filter. |
| makeFilterWrapper | Fuse learner with a feature filter method. |
| makeFixedHoldoutInstance | Generate a fixed holdout instance for resampling. |
| makeImputeMethod | Create a custom imputation method. |
| makeImputeWrapper | Fuse learner with an imputation method. |
| makeLearner | Create learner object. |
| makeLearners | Create multiple learners at once. |
| makeMeasure | Construct performance measure. |
| makeModelMultiplexer | Create model multiplexer for model selection to tune over... |
| makeModelMultiplexerParamSet | Creates a parameter set for model multiplexer tuning. |
| makeMulticlassWrapper | Fuse learner with multiclass method. |
| makeMultilabelBinaryRelevanceWrapper | Use binary relevance method to create a multilabel learner. |
| makeMultilabelClassifierChainsWrapper | Use classifier chains method (CC) to create a multilabel... |
| makeMultilabelDBRWrapper | Use dependent binary relevance method (DBR) to create a... |
| makeMultilabelNestedStackingWrapper | Use nested stacking method to create a multilabel learner. |
| makeMultilabelStackingWrapper | Use stacking method (stacked generalization) to create a... |
| makeOverBaggingWrapper | Fuse learner with the bagging technique and oversampling for... |
| makePreprocWrapper | Fuse learner with preprocessing. |
| makePreprocWrapperCaret | Fuse learner with preprocessing. |
| makeRemoveConstantFeaturesWrapper | Fuse learner with removal of constant features preprocessing. |
| makeResampleDesc | Create a description object for a resampling strategy. |
| makeResampleInstance | Instantiates a resampling strategy object. |
| makeSMOTEWrapper | Fuse learner with SMOTE oversampling for imbalancy correction... |
| makeStackedLearner | Create a stacked learner object. |
| makeTuneControlCMAES | Create control object for hyperparameter tuning with CMAES. |
| makeTuneControlDesign | Create control object for hyperparameter tuning with... |
| makeTuneControlGenSA | Create control object for hyperparameter tuning with GenSA. |
| makeTuneControlGrid | Create control object for hyperparameter tuning with grid... |
| makeTuneControlIrace | Create control object for hyperparameter tuning with Irace. |
| makeTuneControlMBO | Create control object for hyperparameter tuning with MBO. |
| makeTuneControlRandom | Create control object for hyperparameter tuning with random... |
| makeTuneWrapper | Fuse learner with tuning. |
| makeUndersampleWrapper | Fuse learner with simple ove/underrsampling for imbalancy... |
| makeWeightedClassesWrapper | Wraps a classifier for weighted fitting where each class... |
| makeWrappedModel | Induced model of learner. |
| MeasureProperties | Query properties of measures. |
| measures | Performance measures. |
| mergeBenchmarkResults | Merge different BenchmarkResult objects. |
| mergeSmallFactorLevels | Merges small levels of factors into new level. |
| mlrFamilies | mlr documentation families |
| mtcars.task | Motor Trend Car Road Tests clustering task. |
| normalizeFeatures | Normalize features. |
| oversample | Over- or undersample binary classification task to handle... |
| parallelization | Supported parallelization methods |
| performance | Measure performance of prediction. |
| pid.task | PimaIndiansDiabetes classification task. |
| plotBMRBoxplots | Create box or violin plots for a BenchmarkResult. |
| plotBMRRanksAsBarChart | Create a bar chart for ranks in a BenchmarkResult. |
| plotBMRSummary | Plot a benchmark summary. |
| plotCalibration | Plot calibration data using ggplot2. |
| plotCritDifferences | Plot critical differences for a selected measure. |
| plotFilterValues | Plot filter values using ggplot2. |
| plotFilterValuesGGVIS | Plot filter values using ggvis. |
| plotHyperParsEffect | Plot the hyperparameter effects data |
| plotLearnerPrediction | Visualizes a learning algorithm on a 1D or 2D data set. |
| plotLearningCurve | Plot learning curve data using ggplot2. |
| plotLearningCurveGGVIS | Plot learning curve data using ggvis. |
| plotPartialDependence | Plot a partial dependence with ggplot2. |
| plotPartialDependenceGGVIS | Plot a partial dependence using ggvis. |
| plotResiduals | Create residual plots for prediction objects or benchmark... |
| plotROCCurves | Plots a ROC curve using ggplot2. |
| plotThreshVsPerf | Plot threshold vs. performance(s) for 2-class classification... |
| plotThreshVsPerfGGVIS | Plot threshold vs. performance(s) for 2-class classification... |
| plotTuneMultiCritResult | Plots multi-criteria results after tuning using ggplot2. |
| plotTuneMultiCritResultGGVIS | Plots multi-criteria results after tuning using ggvis. |
| plotViperCharts | Visualize binary classification predictions via ViperCharts... |
| Prediction | Prediction object. |
| predictLearner | Predict new data with an R learner. |
| predict.WrappedModel | Predict new data. |
| reduceBatchmarkResults | Reduce results of a batch-distributed benchmark. |
| regr.featureless | Featureless regression learner. |
| regr.randomForest | RandomForest regression learner. |
| reimpute | Re-impute a data set |
| removeConstantFeatures | Remove constant features from a data set. |
| removeHyperPars | Remove hyperparameters settings of a learner. |
| resample | Fit models according to a resampling strategy. |
| ResamplePrediction | Prediction from resampling. |
| ResampleResult | ResampleResult object. |
| RLearner | Internal construction / wrapping of learner object. |
| selectFeatures | Feature selection by wrapper approach. |
| setAggregation | Set aggregation function of measure. |
| setHyperPars | Set the hyperparameters of a learner object. |
| setHyperPars2 | Only exported for internal use. |
| setId | Set the id of a learner object. |
| setLearnerId | Set the ID of a learner object. |
| setPredictThreshold | Set the probability threshold the learner should use. |
| setPredictType | Set the type of predictions the learner should return. |
| setThreshold | Set threshold of prediction object. |
| simplifyMeasureNames | Simplify measure names. |
| smote | Synthetic Minority Oversampling Technique to handle class... |
| sonar.task | Sonar classification task. |
| subsetTask | Subset data in task. |
| summarizeColumns | Summarize columns of data.frame or task. |
| summarizeLevels | Summarizes factors of a data.frame by tabling them. |
| Task | Create a classification, regression, survival, cluster,... |
| TaskDesc | Description object for task. |
| train | Train a learning algorithm. |
| trainLearner | Train an R learner. |
| TuneControl | Control object for tuning |
| TuneMultiCritControl | Create control structures for multi-criteria tuning. |
| TuneMultiCritResult | Result of multi-criteria tuning. |
| tuneParams | Hyperparameter tuning. |
| tuneParamsMultiCrit | Hyperparameter tuning for multiple measures at once. |
| TuneResult | Result of tuning. |
| tuneThreshold | Tune prediction threshold. |
| wpbc.task | Wisonsin Prognostic Breast Cancer (WPBC) survival task. |
| yeast.task | Yeast multilabel classification task. |
