Extract the tuning results from a benchmark result.
Failure model.
ResampleResult object.
Fuse learner with multiclass method.
Internal construction / wrapping of learner object.
Aggregation methods.
Prediction from resampling.
Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
generateLearningCurveData
Generates a learning curve
Subset data in task.
Convert large/infinite numeric values in a data.frame or task.
Create control structures for multi-criteria tuning.
Estimate the residual variance
Benchmark experiment for multiple learners and tasks.
Generate dummy variables for factor features.
Filter features by thresholding filter values.
Returns the filtered features.
Result of multi-criteria tuning.
Wisconsin Breast Cancer classification task
Iris cost-sensitive classification task
Get feature names of task.
getNestedTuneResultsOptPathDf
Get the opt.path
s from each tuning step from the outer resampling.
Show and visualize the steps of feature selection.
Extract the test performance values from a benchmark result.
Get probabilities for some classes.
Generate threshold vs. performance(s) for 2-class classification.
Find matching learning algorithms.
Extract the feature selection results from a benchmark result.
Confusion matrix.
Downsample (subsample) a task or a data.frame.
Get target column of task.
Join some class existing levels to new, larger class levels for classification problems.
Iris classification task
Returns the optimal hyperparameters and optimization path after training.
Create a feature filter
Extract data in task.
Get the id of the task.
Wraps a regression learner for use in cost-sensitive learning.
List of supported learning algorithms.
crossover
getStackedBaseLearnerPredictions
Returns the predictions for each base learner.
plotTuneMultiCritResultGGVIS
Plots multi-criteria results after tuning using ggvis.
Predict new data with an R learner.
Plot threshold vs. performance(s) for 2-class classification using ggvis.
Is the model a FailureModel?
Plots multi-criteria results after tuning using ggplot2.
Induced model of learner.
Remove hyperparameters settings of a learner.
Feature selection by wrapper approach.
Plot learning curve data using ggvis.
Visualize binary classification predictions via ViperCharts system.
Returns a list of mlr's options
Get underlying R model of learner integrated into mlr.
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 imputing constant values shifted below the minimum
usingmin(x) - multiplier * diff(range(x))
,imputeMin(multiplier)
for imputing constant values shifted above the maximum
usingmax(x) + multiplier * diff(range(x))
,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. Result of tuning.
Calculates feature filter values.
Hyperparameter tuning.
Create learner object.
Construct performance measure.
Converts predictions to a format package ROCR can handle.
Extract costs in task.
Generate a fixed holdout instance for resampling.
Get predictions from resample results.
Convert arguments to control structure.
Return task ids used in benchmark.
Result of a benchmark run.
Fuse learner with the bagging technique.
Get formula of a task.
Fuse learner with simple downsampling (subsampling).
Create control structures for feature selection.
Extract the aggregated performance values from a benchmark result.
Fuse learner with the bagging technique and oversampling for imbalancy correction.
Calculates feature filter values.
Merges small levels of factors into new level.
Create a custom imputation method.
Plot threshold vs. performance(s) for 2-class classification using ggplot2.
makeCostSensClassifWrapper
Wraps a classification learner for use in cost-sensitive learning.
Return error message of FailureModel.
Get number of observations in task.
Performance measures.
Over- or undersample binary classification task to handle class imbalancy.
Set threshold of prediction object.
Find matching measures.
PimaIndiansDiabetes classification task
Tune prediction threshold.
Set aggregation function of measure.
European Union Agricultural Workforces clustering task
Description object for task.
Aggregation object.
Prediction object.
Predict new data.
Boston Housing regression task
Summarizes factors of a data.frame by tabling them.
Result of feature selection.
Returns the selected feature set and optimization path after training.
Only exported for internal use.
Create control structures for tuning.
Get number of features in task.
NCCTG Lung Cancer survival task
Synthetic Minority Oversampling Technique to handle class imbalancy in binary classification.
Train an R learner.
Get a summarizing task description.
Configures the behavior of the package.
Get the tuned hyperparameter settings from a nested tuning.
Set, add, remove or query properties of learners
Measure performance of prediction.
Fuse learner with simple ove/underrsampling for imbalancy correction in binary classification.
Extract the predictions from a benchmark result.
Fuse learner with preprocessing.
makeCustomResampledMeasure
Construct your own resampled performance measure.
Re-impute a data set
Return learner ids used in benchmark.
Motor Trend Car Road Tests clustering task
Create a stacked learner object.
Create a classification, regression, survival, cluster, or cost-sensitive classification task.
Extract the feature selection results from a benchmark result.
Plot filter values using ggvis.
Get the name(s) of the target column(s).
Get current parameter settings for a learner.
Get a description of all possible parameter settings for a learner.
Plot learning curve data using ggplot2.
Wisonsin Prognostic Breast Cancer (WPBC) survival task
Summarize columns of data.frame or task.
Impute and re-impute data
List filter methods
getHomogeneousEnsembleModels
Returns the list of fitted models.
Set the id of a learner object.
Fuse learner with preprocessing
Specifiy your own aggregation of measures
Visualizes a learning algorithm on a 1D or 2D data set.
Plot filter values using ggplot2.
Create a description object for a resampling strategy.
Set the hyperparameters of a learner object.
Creates a measure for non-standard misclassification costs.
Generate binary classification predictions via ROCR ROC curves.
Set the probability threshold the learner should use.
Normalize features
Create model multiplexer for model selection to tune over multiple possible models.
Hyperparameter tuning for multiple measures at once.
Plots results from generateROCRCurvesData using ggplot2.
makeWeightedClassesWrapper
Wraps a classifier for weighted fitting where each class receives a weight.
Fuse learner with an imputation method.
Fuse learner with a feature filter method.
Instantiates a resampling strategy object.
Drop some features of task.
Fit models according to a resampling strategy.
Set the type of predictions the learner should return.
Plots results from generateROCRCurvesData using ggvis.
Remove constant features from a data set.
Fuse learner with feature selection.
Get the type of the task.
Train a learning algorithm.
makeModelMultiplexerParamSet
Creates a parameter set for model multiplexer tuning.
Sonar classification task
makeCostSensWeightedPairsWrapper
Wraps a classifier for cost-sensitive learning to produce a weighted pairs model.
Fuse learner with tuning.