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