Create a cluster task.
Failure model.
BenchmarkResult object.
Result of feature selection.
Aggregation object.
Create a classification task.
Confusion matrix
Query properties of learners.
Create control structures for feature selection.
Create a cost-sensitive classification task.
Prediction object.
Internal construction / wrapping of learner object.
Create a regression task.
Prediction from resampling.
Create a classification, regression, survival, cluster, cost-sensitive classification or
multilabel task.
Description object for task.
Query properties of measures.
Show and visualize the steps of feature selection.
Converts predictions to a format package ROCR can handle.
Create a multilabel task.
European Union Agricultural Workforces clustering task.
Result of multi-criteria tuning.
ResampleResult object.
Wisconsin Breast Cancer classification task.
Result of tuning.
Benchmark experiment for multiple learners and tasks.
Confusion matrix.
Run machine learning benchmarks as distributed experiments.
Calculate receiver operator measures.
Create control structures for multi-criteria tuning.
checkPredictLearnerOutput
Check output returned by predictLearner.
Control object for tuning
Generate dummy variables for factor features.
Convert large/infinite numeric values in a data.frame or task.
Create a survival task.
Compute new measures for existing ResampleResult
Exported for internal use only.
createSpatialResamplingPlots
Create (spatial) resampling plot objects.
Drop some features of task.
Extract features from functional data.
estimateRelativeOverfitting
Estimate relative overfitting.
Extract functional principal component analysis features.
DTW kernel features
Get or delete mlr cache directory
Aggregation methods.
Crossover.
Boston Housing regression task.
Downsample (subsample) a task or a data.frame.
FuelSubset functional data regression task.
generateFeatureImportanceData
Generate feature importance.
generateCritDifferencesData
Generate data for critical-differences plot.
Discrete Wavelet transform features.
Filter features by thresholding filter values.
Extract the aggregated performance values from a benchmark result.
Generate threshold vs. performance(s) for 2-class classification.
Generate classifier calibration data.
Return learners used in benchmark.
Return learner ids used in benchmark.
Fast Fourier transform features.
Extract the test performance values from a benchmark result.
Return learner short.names used in benchmark.
Return measures IDs used in benchmark.
Extract the predictions from a benchmark result.
Convert a machine learning benchmark / demo object from package mlbench to a task.
Iris cost-sensitive classification task.
Calculates feature filter values.
extractFDAMultiResFeatures
Multiresolution feature extraction.
Time-Series Feature Heuristics
Change Task Data
Confusion matrix.
Extract all task descriptions from benchmark result (DEPRECATED).
Extract all task descriptions from benchmark result.
Get the note for the learner.
Get underlying R model of learner integrated into mlr.
generateLearningCurveData
Generates a learning curve.
Provides out-of-bag predictions for a given model and the corresponding learner.
Extracts out-of-bag predictions from trained models.
Returns the selected feature set and optimization path after training.
Return measures used in benchmark.
generatePartialDependenceData
Generate partial dependence.
Configures the behavior of the package.
Get task description from resample results (DEPRECATED).
Get the resampling indices from a tuning or feature selection wrapper..
Convert BenchmarkResult to a rank-matrix.
Estimate the residual variance.
Extract all models from benchmark result.
Get current parameter settings for a learner.
Get the ID of the learner.
Calculates feature importance values for trained models.
Bspline mlq features
generateHyperParsEffectData
Generate hyperparameter effect data.
Get default measure.
Get the short name of the learner.
Get only functional features from a task or a data.frame.
getHomogeneousEnsembleModels
Deprecated, use getLearnerModel
instead.
Get tuning parameters from a learner of the caret R-package.
Get the type of the learner.
getNestedTuneResultsOptPathDf
Get the opt.path
s from each tuning step from the outer resampling.
Get the tuned hyperparameter settings from a nested tuning.
Get the class levels for classification and multilabel tasks.
getStackedBaseLearnerPredictions
Returns the predictions for each base learner.
Get feature names of task.
Get the id of the task.
Get the class weight parameter of a learner.
Returns a list of mlr's options.
Get number of features in task.
Return error message of FailureModel.
Return the error dump of FailureModel.
Get target data of task.
Perform overall Friedman test for a BenchmarkResult.
Extract the feature selection results from a benchmark result.
Perform a posthoc Friedman-Nemenyi test.
Extract the feature selection results from a benchmark result.
Gunpoint functional data classification task.
Check whether the object contains functional features.
Get formula of a task.
Get specific help for a learner's parameters.
Built-in imputation methods.
Get the type of the task.
getPredictionProbabilities
Get probabilities for some classes.
getMultilabelBinaryPerformances
Retrieve binary classification measures for multilabel classification predictions.
Return task ids used in benchmark.
Extract the tuning results from a benchmark result.
Get response / truth from prediction object.
Get predictions from resample results.
Get the required R packages of the learner.
getFeatureImportanceLearner.regr.randomForestSRC
Calculates feature importance values for a given learner.
Returns the optimal hyperparameters and optimization path after training.
Get the optimization path of a tuning result.
Get the parameter values of the learner.
List the supported learner properties
makeCostSensWeightedPairsWrapper
Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
List the supported task types in mlr
Get a description of all possible parameter settings for a learner.
Wraps a regression learner for use in cost-sensitive learning.
Get the parameter set of the learner.
NCCTG Lung Cancer survival task.
Get the predict type of the learner.
Returns the filtered features.
Find matching learning algorithms.
Convert arguments to control structure.
List of supported learning algorithms.
Fuse learner with feature selection.
makeExtractFDAFeatsWrapper
Fuse learner with an extractFDAFeatures method.
Get summarizing task description from prediction.
Get task description from resample results (DEPRECATED).
Return the error dump of a failed Prediction.
Deprecated, use getPredictionProbabilities
instead.
Return the error dump of ResampleResult.
Specify your own aggregation of measures.
List the supported measure properties.
Get list of predictions for train and test set of each single resample iteration.
Get a summarizing task description.
Extract costs in task.
Fuse learner with the bagging technique.
Creates a measure for non-standard misclassification costs.
Find matching measures.
makeCostSensClassifWrapper
Wraps a classification learner for use in cost-sensitive learning.
Deprecated, use getTaskDesc instead. Deprecated, use hasLearnerProperties
instead.
Fuse learner with dummy feature creator.
Constructor for FDA feature extraction methods.
Fuse learner with an imputation method.
Get number of observations in task.
Extract data in task.
Get the name(s) of the target column(s).
Iris classification task.
Impute and re-impute data
Create learner object.
Fuse learner with a feature filter method.
Generate a fixed holdout instance for resampling.
makeMultilabelNestedStackingWrapper
Use nested stacking method to create a multilabel learner.
Access help page of learner functions.
Is the model a FailureModel?
List filter methods.
makeClassificationViaRegressionWrapper
Classification via regression wrapper.
listFilterEnsembleMethods
List ensemble filter methods.
Join some class existing levels to new, larger class levels for classification problems.
Fuse learner with the bagging technique and oversampling for imbalancy correction.
Wraps a classification learner to support problems where the class label is (almost) constant.
Fuse learner with preprocessing.
Exported for internal use only.
Instantiates a resampling strategy object.
makeCustomResampledMeasure
Construct your own resampled performance measure.
Only exported for internal use.
Create a feature filter.
Fuse learner with simple downsampling (subsampling).
makeMultilabelStackingWrapper
Use stacking method (stacked generalization) to create a multilabel learner.
Exported for internal use.
Create an ensemble feature filter.
Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
makeRLearner.classif.fdausc.glm
Classification of functional data by Generalized Linear Models.
Fuse learner with preprocessing.
Create multiple learners at once.
Construct performance measure.
makeRLearner.classif.fdausc.kernel
Learner for kernel classification for functional data.
Create control object for hyperparameter tuning with CMAES.
makeWeightedClassesWrapper
Wraps a classifier for weighted fitting where each class receives a weight.
makeMultilabelClassifierChainsWrapper
Use classifier chains method (CC) to create a multilabel learner.
Induced model of learner.
Phoneme functional data multilabel classification task.
Create control object for hyperparameter tuning with grid search.
Create control object for hyperparameter tuning with Irace.
makeRLearner.classif.fdausc.np
Learner for nonparametric classification for functional data.
Create control object for hyperparameter tuning with predefined design.
Normalize features.
Plot a benchmark summary.
Over- or undersample binary classification task to handle class imbalancy.
PimaIndiansDiabetes classification task.
Reduce results of a batch-distributed benchmark.
Re-extract features from a data set
Use dependent binary relevance method (DBR) to create a multilabel learner.
Supported parallelization methods
Create a stacked learner object.
Exported for internal use.
Remove constant features from a data set.
Re-impute a data set
Measure performance of prediction.
Subset data in task.
Plot calibration data using ggplot2.
Create control object for hyperparameter tuning with GenSA.
J. Muenchow's Ecuador landslide data set
Create a data.frame containing functional features from a normal data.frame.
Performance measures.
Create a custom imputation method.
Set the hyperparameters of a learner object.
Only exported for internal use.
Merges small levels of factors into new level.
Create control object for hyperparameter tuning with random search.
Create control object for hyperparameter tuning with MBO.
Set parameters of performance measures
Create box or violin plots for a BenchmarkResult.
mlr: Machine Learning in R
Merge different BenchmarkResult objects.
Yeast multilabel classification task.
Sonar classification task.
Plots multi-criteria results after tuning using ggplot2.
Plot threshold vs. performance(s) for 2-class classification using ggplot2.
Set the ID of a learner object.
Set the id of a learner object.
Plot critical differences for a selected measure.
Spam classification task.
Create a bar chart for ranks in a BenchmarkResult.
Summarize columns of data.frame or task.
Plot filter values using ggplot2.
Set the probability threshold the learner should use.
Summarizes factors of a data.frame by tabling them.
Predict new data.
Predict new data with an R learner.
Train an R learner.
Train a learning algorithm.
Fuse learner with multiclass method.
Create model multiplexer for model selection to tune over multiple
possible models.
makeModelMultiplexerParamSet
Creates a parameter set for model multiplexer tuning.
makeMultilabelBinaryRelevanceWrapper
Use binary relevance method to create a multilabel learner.
Plot learning curve data using ggplot2.
makeRemoveConstantFeaturesWrapper
Fuse learner with removal of constant features preprocessing.
Create a description object for a resampling strategy.
Fuse learner with tuning.
Plot a partial dependence with ggplot2.
Feature selection by wrapper approach.
Simplify measure names.
Hyperparameter tuning.
Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
Set aggregation function of measure.
Hyperparameter tuning for multiple measures at once.
Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
mlr documentation families
Plot the hyperparameter effects data
Motor Trend Car Road Tests clustering task.
Visualizes a learning algorithm on a 1D or 2D data set.
Plots a ROC curve using ggplot2.
Create residual plots for prediction objects or benchmark results.
Remove hyperparameters settings of a learner.
Fit models according to a resampling strategy.
Set threshold of prediction object.
Tune prediction threshold.
Set the type of predictions the learner should return.
Wisonsin Prognostic Breast Cancer (WPBC) survival task.