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