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