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Objects of the class learner represent learning systems that can be used in the routines designed to carry out experimental comparisons within the DMwR package.
● Data Source:
CranContrib
● Keywords: classes
● Alias: learner, learner-class, show,learner-method
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This function handles unbalanced classification problems using the SMOTE method. Namely, it can generate a new "SMOTEd" data set that addresses the class unbalance problem. Alternatively, it can also run a classification algorithm on this new data set and return the resulting model.
● Data Source:
CranContrib
● Keywords: models
● Alias: SMOTE
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This function transforms a set of numeric values into a set of trading signals according to two thresholds: one that establishes the limit above which any value will be transformed into a buy signal ('b'), and the other that sets the value below which we have a sell signal ('s'). Between the two thresholds we will have a hold signal ('h').
● Data Source:
CranContrib
● Keywords: models
● Alias: trading.signals
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This function implements the sliding window learning method that is frequently used in time series forecasting. The function allows applying this methodology to any modelling technique. The function returns the predictions of this technique, when applied using a sliding window approach, for the given test set.
● Data Source:
CranContrib
● Keywords: models
● Alias: slidingWindowTest
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This function produces a vector with the names of the datasets involved in an experimental comparison
● Data Source:
CranContrib
● Keywords: models
● Alias: dsNames
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This is the class of the objects holding the results of a cross validation experiment.
● Data Source:
CranContrib
● Keywords: classes
● Alias: cvRun, cvRun-class, plot,cvRun,missing-method, summary,cvRun-method
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This class of objects contains the information describing a bootstrap experiment, i.e. its settings.
● Data Source:
CranContrib
● Keywords: classes
● Alias: bootSettings, bootSettings-class, show,bootSettings-method
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This is the class of the objects storing the results of a bootstrap experiment.
● Data Source:
CranContrib
● Keywords: classes
● Alias: bootRun, bootRun-class, summary,bootRun-method
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Precision/recall (PR) curves are visual representations of the performance of a classification model in terms of the precision and recall statistics. The curves are obtained by proper interpolation of the values of the statistics at different working points. These working points can be given by different cut-off limits on a ranking of the class of interest provided by the model.
● Data Source:
CranContrib
● Keywords: models
● Alias: PRcurve
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This function computes the reachability measure for each instance of a dataset. This result is used later to compute the Local Outlyingness Factor.
● Data Source:
CranContrib
● Keywords: models
● Alias: reachability
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providing 
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