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