Generates the threshold curve data from the evaluation object’s predictions.
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Returns: | the generated threshold curve data |
Return type: | Instances |
Calculates the area under the ROC curve (AUC).
Parameters: | data (Instances) – the threshold curve data |
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Returns: | the area |
Return type: | float |
Calculates the area under the precision recall curve (PRC).
Parameters: | data (Instances) – the threshold curve data |
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Returns: | the area |
Return type: | float |
Retrieves x and y columns from of the data generated by the weka.classifiers.evaluation.ThresholdCurve class.
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Returns: | tuple of x and y arrays |
Return type: | tuple |
Plots the classifers for the given list of predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
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Plots a learning curve.
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Plots the PRC (precision recall) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
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Plots the ROC (receiver operator characteristics) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
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Plots the cluster assignments against the specified attributes.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
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Uses the internal format to plot the dataset, one line per instance.
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Plots all attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
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Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
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Plots the results from an experiment.
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