MultilayerPerceptronClassificationTrainingSummary¶
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class
pyspark.ml.classification.MultilayerPerceptronClassificationTrainingSummary(java_obj: Optional[JavaObject] = None)[source]¶ Abstraction for MultilayerPerceptronClassifier Training results.
New in version 3.1.0.
Methods
fMeasureByLabel([beta])Returns f-measure for each label (category).
weightedFMeasure([beta])Returns weighted averaged f-measure.
Attributes
Returns accuracy.
Returns false positive rate for each label (category).
Field in “predictions” which gives the true label of each instance.
Returns the sequence of labels in ascending order.
Objective function (scaled loss + regularization) at each iteration.
Returns precision for each label (category).
Field in “predictions” which gives the prediction of each class.
Dataframe outputted by the model’s transform method.
Returns recall for each label (category).
Number of training iterations until termination.
Returns true positive rate for each label (category).
Field in “predictions” which gives the weight of each instance as a vector.
Returns weighted false positive rate.
Returns weighted averaged precision.
Returns weighted averaged recall.
Returns weighted true positive rate.
Methods Documentation
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fMeasureByLabel(beta: float = 1.0) → List[float]¶ Returns f-measure for each label (category).
New in version 3.1.0.
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weightedFMeasure(beta: float = 1.0) → float¶ Returns weighted averaged f-measure.
New in version 3.1.0.
Attributes Documentation
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accuracy¶ Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)
New in version 3.1.0.
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falsePositiveRateByLabel¶ Returns false positive rate for each label (category).
New in version 3.1.0.
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labelCol¶ Field in “predictions” which gives the true label of each instance.
New in version 3.1.0.
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labels¶ Returns the sequence of labels in ascending order. This order matches the order used in metrics which are specified as arrays over labels, e.g., truePositiveRateByLabel.
New in version 3.1.0.
Notes
In most cases, it will be values {0.0, 1.0, …, numClasses-1}, However, if the training set is missing a label, then all of the arrays over labels (e.g., from truePositiveRateByLabel) will be of length numClasses-1 instead of the expected numClasses.
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objectiveHistory¶ Objective function (scaled loss + regularization) at each iteration. It contains one more element, the initial state, than number of iterations.
New in version 3.1.0.
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precisionByLabel¶ Returns precision for each label (category).
New in version 3.1.0.
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predictionCol¶ Field in “predictions” which gives the prediction of each class.
New in version 3.1.0.
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predictions¶ Dataframe outputted by the model’s transform method.
New in version 3.1.0.
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recallByLabel¶ Returns recall for each label (category).
New in version 3.1.0.
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totalIterations¶ Number of training iterations until termination.
New in version 3.1.0.
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truePositiveRateByLabel¶ Returns true positive rate for each label (category).
New in version 3.1.0.
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weightCol¶ Field in “predictions” which gives the weight of each instance as a vector.
New in version 3.1.0.
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weightedFalsePositiveRate¶ Returns weighted false positive rate.
New in version 3.1.0.
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weightedPrecision¶ Returns weighted averaged precision.
New in version 3.1.0.
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weightedRecall¶ Returns weighted averaged recall. (equals to precision, recall and f-measure)
New in version 3.1.0.
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weightedTruePositiveRate¶ Returns weighted true positive rate. (equals to precision, recall and f-measure)
New in version 3.1.0.
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