plotThreshVsPerfGGVIS
(Package: mlr) :
Plot threshold vs. performance(s) for 2-class classification using ggvis.
Plots threshold vs. performance(s) data that has been generated with generateThreshVsPerfData.
● Data Source:
CranContrib
● Keywords:
● Alias: plotThreshVsPerfGGVIS
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Contains predictions from resampling, returned (among other stuff) by function resample. Can basically be used in the same way as Prediction, its super class. The main differences are: (a) The internal data.frame (member data) contains an additional column iter, specifying the iteration of the resampling strategy, and and additional columns set, specifying whether the prediction was from an observation in the “train” or “test” set. (b) The prediction time is a numeric vector, its length equals the number of iterations.
● Data Source:
CranContrib
● Keywords:
● Alias: ResamplePrediction
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makeSMOTEWrapper
(Package: mlr) :
Fuse learner with SMOTE oversampling for imbalancy correction in binary classification.
Creates a learner object, which can be used like any other learner object. Internally uses smote before every model fit.
● Data Source:
CranContrib
● Keywords:
● Alias: makeSMOTEWrapper
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makeWeightedClassesWrapper
(Package: mlr) :
Wraps a classifier for weighted fitting where each class receives a weight.
Creates a wrapper, which can be used like any other learner object.
● Data Source:
CranContrib
● Keywords:
● Alias: makeWeightedClassesWrapper
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getTaskNFeats
(Package: mlr) :
Get number of features in task.
Get number of features in task.
● Data Source:
CranContrib
● Keywords:
● Alias: getTaskNFeats
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plotLearnerPrediction
(Package: mlr) :
Visualizes a learning algorithm on a 1D or 2D data set.
Trains the model for 1 or 2 selected features, then displays it via ggplot. Good for teaching or exploring models.
● Data Source:
CranContrib
● Keywords:
● Alias: plotLearnerPrediction
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getLearnerModel
(Package: mlr) :
Get underlying R model of learner integrated into mlr.
Get underlying R model of learner integrated into mlr.
● Data Source:
CranContrib
● Keywords:
● Alias: getLearnerModel
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getCaretParamSet
(Package: mlr) :
Get tuning parameters from a learner of the caret R-package.
Constructs a grid of tuning parameters from a learner of the caret R-package. These values are then converted into a list of non-tunable parameters (par.vals) and a tunable ParamSet (par.set), which can be used by tuneParams for tuning the learner. Numerical parameters will either be specified by their lower and upper bounds or they will be discretized into specific values.
● Data Source:
CranContrib
● Keywords:
● Alias: getCaretParamSet
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predictLearner
(Package: mlr) :
Predict new data with an R learner.
Mainly for internal use. Predict new data with a fitted model. You have to implement this method if you want to add another learner to this package.
● Data Source:
CranContrib
● Keywords:
● Alias: predictLearner
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plotBMRRanksAsBarChart
(Package: mlr) :
Create a bar chart for ranks in a BenchmarkResult.
Plots a bar chart from the ranks of algorithms. Alternatively, tiles can be plotted for every rank-task combination, see pos for details. In all plot variants the ranks of the learning algorithms are displayed on the x-axis. Areas are always colored according to the learner.id.
● Data Source:
CranContrib
● Keywords:
● Alias: plotBMRRanksAsBarChart
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providing 
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