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