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