Benchmark experiment for multiple learners and tasks.
Convert large/infinite numeric values in a data.frame or task.
Convert a machine learning benchmark / demo object from package mlbench to a task.
Featureless classification learner.
Iris cost-sensitive classification task.
Drop some features of task.
Downsample (subsample) a task or a data.frame.
Crossover.
Configures the behavior of the package.
Generate dummy variables for factor features.
Convert BenchmarkResult to a rank-matrix.
Perform overall Friedman test for a BenchmarkResult.
Create control structures for feature selection.
generateFeatureImportanceData
Generate feature importance.
Perform a posthoc Friedman-Nemenyi test.
estimateRelativeOverfitting
Estimate relative overfitting.
generateCritDifferencesData
Generate data for critical-differences plot.
Generate classifier calibration data.
Estimate the residual variance.
Failure model.
Filter features by thresholding filter values.
Extract the feature selection results from a benchmark result.
Calculates feature filter values.
generateHyperParsEffectData
Generate hyperparameter effect data.
Return learner ids used in benchmark.
Generate threshold vs. performance(s) for 2-class classification.
generateLearningCurveData
Generates a learning curve.
Extract the feature selection results from a benchmark result.
generateFunctionalANOVAData
Generate a functional ANOVA decomposition
Extract the aggregated performance values from a benchmark result.
generatePartialDependenceData
Generate partial dependence.
Extract the tuning results from a benchmark result.
Extract the test performance values from a benchmark result.
Return measures used in benchmark.
Return task ids used in benchmark.
Extract all task descriptions from benchmark result.
Return measures IDs used in benchmark.
Return learners used in benchmark.
Extract all models from benchmark result.
Return learner short.names used in benchmark.
Extract the predictions from a benchmark result.
Returns the selected feature set and optimization path after training.
Returns the filtered features.
Calculates feature filter values.
Get tuning parameters from a learner of the caret R-package.
Get the class weight parameter of a learner.
getHomogeneousEnsembleModels
Deprecated, use
Get current parameter settings for a learner.
Confusion matrix.
Return error message of FailureModel.
Get default measure.
Returns a list of mlr's options.
Get task description from resample results.
Deprecated, use
Get the class levels for classification and multilabel tasks.
Extract costs in task.
Get the required R packages of the learner.
getStackedBaseLearnerPredictions
Returns the predictions for each base learner.
Get the parameter set of the learner.
Get response / truth from prediction object.
getMultilabelBinaryPerformances
Retrieve binary classification measures for multilabel classification predictions.
Calculates feature importance values for trained models.
Get the short name of the learner.
getFeatureImportanceLearner.regr.randomForestSRC
Calculates feature importance values for a given learner.
Get predictions from resample results.
Get list of predictions for train and test set of each single resample iteration.
Get number of features in task.
Get the name(s) of the target column(s).
Get number of observations in task.
Get the id of the task.
Get the type of the learner.
List filter methods.
Find matching learning algorithms.
Fuse learner with feature selection.
Create model multiplexer for model selection to tune over multiple possible models.
makeModelMultiplexerParamSet
Creates a parameter set for model multiplexer tuning.
Get a description of all possible parameter settings for a learner.
getPredictionProbabilities
Get probabilities for some classes.
makeWeightedClassesWrapper
Wraps a classifier for weighted fitting where each class receives a weight.
Induced model of learner.
Fuse learner with simple downsampling (subsampling).
Fuse learner with the bagging technique.
Specify your own aggregation of measures.
Show and visualize the steps of feature selection.
Fuse learner with a feature filter method.
Fuse learner with the bagging technique and oversampling for imbalancy correction.
Fuse learner with preprocessing.
Create a feature filter.
makeMultilabelBinaryRelevanceWrapper
Use binary relevance method to create a multilabel learner.
European Union Agricultural Workforces clustering task.
Fuse learner with multiclass method.
Plot filter values using ggplot2.
Plot filter values using ggvis.
Plot the hyperparameter effects data
Get the ID of the learner.
Boston Housing regression task.
Summarize columns of data.frame or task.
Subset data in task.
Visualizes a learning algorithm on a 1D or 2D data set.
Feature selection by wrapper approach.
Internal construction / wrapping of learner object.
getNestedTuneResultsOptPathDf
Get the
Get underlying R model of learner integrated into mlr.
Get the tuned hyperparameter settings from a nested tuning.
Get formula of a task.
Get feature names of task.
Iris classification task.
Is the model a FailureModel?
Query properties of learners.
Aggregation methods.
Compute new measures for existing ResampleResult
Get the type of the task.
makeCustomResampledMeasure
Construct your own resampled performance measure.
Create learner object.
Fuse learner with preprocessing.
makeCostSensWeightedPairsWrapper
Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
Get target data of task.
Fuse learner with an imputation method.
Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
Fuse learner with tuning.
makeRemoveConstantFeaturesWrapper
Fuse learner with removal of constant features preprocessing.
PimaIndiansDiabetes classification task.
Create box or violin plots for a BenchmarkResult.
Plot a partial dependence with ggplot2.
plotPartialDependenceGGVIS
Plot a partial dependence using ggvis.
Set aggregation function of measure.
regression using randomForest.
Re-impute a data set
Set the hyperparameters of a learner object.
Confusion matrix.
Calculate receiver operator measures.
Get the parameter values of the learner.
Built-in imputation methods.
Impute and re-impute data
Join some class existing levels to new, larger class levels for classification problems.
Extract data in task.
Get a summarizing task description.
Get the predict type of the learner.
Merges small levels of factors into new level.
mlr documentation families
Plots multi-criteria results after tuning using ggplot2.
Fit models according to a resampling strategy.
plotTuneMultiCritResultGGVIS
Plots multi-criteria results after tuning using ggvis.
Set the type of predictions the learner should return.
Set threshold of prediction object.
Tune prediction threshold.
Hyperparameter tuning for multiple measures at once.
Prediction from resampling.
Wisconsin Breast Cancer classification task.
Converts predictions to a format package ROCR can handle.
Returns the optimal hyperparameters and optimization path after training.
Deprecated, use
Find matching measures.
Wraps a classification learner to support problems where the class label is (almost) constant.
Creates a measure for non-standard misclassification costs.
Generate a fixed holdout instance for resampling.
Create a custom imputation method.
NCCTG Lung Cancer survival task.
makeCostSensClassifWrapper
Wraps a classification learner for use in cost-sensitive learning.
Wraps a regression learner for use in cost-sensitive learning.
List of supported learning algorithms.
Create multiple learners at once.
Construct performance measure.
Plot threshold vs. performance(s) for 2-class classification using ggplot2.
Plot threshold vs. performance(s) for 2-class classification using ggvis.
makeMultilabelStackingWrapper
Use stacking method (stacked generalization) to create a multilabel learner.
Plot calibration data using ggplot2.
Plot critical differences for a selected measure.
makeMultilabelNestedStackingWrapper
Use nested stacking method to create a multilabel learner.
Predict new data.
Visualize binary classification predictions via ViperCharts system.
Set the ID of a learner object.
Set the probability threshold the learner should use.
Summarizes factors of a data.frame by tabling them.
Merge different BenchmarkResult objects.
Train a learning algorithm.
Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
Create a stacked learner object.
Performance measures.
Create a bar chart for ranks in a BenchmarkResult.
Plot a benchmark summary.
Plot learning curve data using ggplot2.
Plot learning curve data using ggvis.
Predict new data with an R learner.
makeMultilabelClassifierChainsWrapper
Use classifier chains method (CC) to create a multilabel learner.
Use dependent binary relevance method (DBR) to create a multilabel learner.
Convert arguments to control structure.
Featureless regression learner.
Motor Trend Car Road Tests clustering task.
Over- or undersample binary classification task to handle class imbalancy.
Normalize features.
Create residual plots for prediction objects or benchmark results.
Measure performance of prediction.
Plots a ROC curve using ggplot2.
Remove hyperparameters settings of a learner.
Remove constant features from a data set.
Yeast multilabel classification task.
Wisonsin Prognostic Breast Cancer (WPBC) survival task.
Train an R learner.
Create control structures for tuning.
Create control structures for multi-criteria tuning.
Hyperparameter tuning.
Only exported for internal use.
Set the id of a learner object.
Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
Sonar classification task.
BenchmarkResult object.
Aggregation object.
Confusion matrix
Result of feature selection.
Instantiates a resampling strategy object.
Create a description object for a resampling strategy.
ResampleResult object.
Prediction object.
Create a classification, regression, survival, cluster, cost-sensitive classification or
Description object for task.
Result of tuning.
Result of multi-criteria tuning.