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