Get number of feature in task.
Result of a benchmark run.
Get a description of all possible parameter settings for a learner.
Configures the behavior of the package.
makeTuneMultiCritControlGrid
Create control structures for multi-criteria tuning.
Set, add, remove or query properties of learners
crossover
Prediction object.
Create a classification, regression, survival, cluster, or cost-sensitive classification task.
Drop some features of task.
Get target column of task.
Returns all available feature filter methods.
makeCostSensWeightedPairsWrapper
Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
Returns the list of fitted models.
Return error message of FailureModel.
Estimate the residual variance
Calculates feature filter values.
An aggregation method reduces the performance values of the test
(and possibly the training sets) to a single value.
To see all possible, implemented aggregations look at aggregations
. Result of multi-criteria tuning.
Generate dummy variables for factor features.
Extract costs in task.
Sonar classification task
Fuse learner with feature selection.
Result of tuning.
Internal construction / wrapping of learner object.
Result of feature selection.
Description object for task.
Extract the predictions from a benchmark result.
Aggregation methods.
Failure model.
Downsample (subsample) a task or a data.frame.
Create a custom imputation method.
Show and visualize the steps of feature selection.
Instantiates a resampling strategy object.
Built in imputation methods
The built-ins are:
imputeConstant(const)
for imputation using a constant value,imputeMedian()
for imputation using the median,imputeMode()
for imputation using the mode,imputeMin(multiplier)
for imputation using the minimum,imputeMax(multiplier)
for imputation using the maximum,imputeNormal(mean, sd)
for imputation using normally
distributed random values. Mean and standard deviation will be calculated
from the data if not provided.imputeHist(breaks, use.mids)
for imputation using random values
with probabilities calculated usingtable
orhist
.imputeLearner(learner, preimpute)
for imputations using the response
of a classification or regression learner. Fuse learner with the bagging technique and oversampling for imbalancy correction.
Specifiy your own aggregation of measures
Only exported for internal use.
Prediction from resampling.
Create control structures for tuning.
Set the id of a learner object.
Plots multi-criteria results after tuning.
Subset data in task.
Predict new data.
Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
Create learner object.
Find matching measures.
Hyperparameter tuning for multiple measures at once.
Set the type of predictions the learner should return.
Is the model a FailureModel?
Train an R learner.
Induced model of learner.
Remove hyperparameters settings of a learner.
Returns the optimal hyperparameters and optimization path after training or benchmarking.
Fuse learner with the bagging technique.
Returns a list of mlr's options
Set aggregation function of measure.
Fuse learner with a feature filter method.
Display all possible hyperparameter settings for a learner that mlr knows.
Returns the list of models fitted in bagging.
Extract the aggregated measures of a benchmark result.
Plot threshold vs. performance(s) for 2-class classification.
Creates a measure for non-standard misclassification costs.
Re-impute a data set
Fit models according to a resampling strategy.
Create model multiplexer for model selection to tune over multiple possible models.
Visualizes a learning algorithm on a 1D or 2D data set.
Convert arguments to control structure.
Fuse learner with preprocessing.
Filter features by thresholding filter values.
Extract performance measures of bechmark result.
makeModelMultiplexerParamSet
Creates a parameter set for model multiplexer tuning.
Boston Housing regression task
Train a learning algorithm.
Fuse learner with simple downsampling (subsampling).
Predict new data with an R learner.
Remove constant features from a data set.
Returns the filtered features.
Normalize features
Get underlying R model of learner integrated into mlr.
Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
Get formula of a task.
Over- or undersample binary classification task to handle class imbalancy.
Converts predictions to a format package ROCR can handle.
Set threshold of prediction object.
makeWeightedClassesWrapper
Wraps a classifier for weighted fitting where each class receives a weight.
Get current parameter settings for a learner.
Impute and re-impute data
Create a description object for a resampling strategy.
Fuse learner with an imputation method.
makeFeatSelControlExhaustive
Create control structures for feature selection.
Plot filter values.
Set the hyperparameters of a learner object.
Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
Extract data in task.
Get feature names of task.
Hyperparameter tuning.
Measure performance of prediction.
Feature selection by wrapper approach.
makeCustomResampledMeasure
Construct your own resampled performance measure.
Generate a fixed holdout instance for resampling.
Confusion matrix.
Get probabilities for some classes.
Tune prediction threshold.
Benchmark experiment for multiple learners and tasks.
Construct performance measure.
getCostSensWeightedPairsModels
Returns the list of fitted models.
makeCostSensClassifWrapper
Wraps a classification learner for use in cost-sensitive learning.
Wraps a regression learner for use in cost-sensitive learning.
Returns the selected feature set and optimization path after training or benchmarking.
Iris classification task
Fuse learner with tuning.
Returns the underlying classification model.
Returns a filter result after training or benchmarking.
Find matching learning algorithms.
List of supported learning algorithms.
Performance measures.