RandomForestClassificationTrainingSummary¶

class pyspark.ml.classification.RandomForestClassificationTrainingSummary(java_obj: Optional[JavaObject] = None)¶

Abstraction for RandomForestClassificationTraining Training results.

Methods

`fMeasureByLabel`([beta])	Returns f-measure for each label (category).
`weightedFMeasure`([beta])	Returns weighted averaged f-measure.

Attributes

`accuracy`	Returns accuracy.
`falsePositiveRateByLabel`	Returns false positive rate for each label (category).
`labelCol`	Field in “predictions” which gives the true label of each instance.
`labels`	Returns the sequence of labels in ascending order.
`objectiveHistory`	Objective function (scaled loss + regularization) at each iteration.
`precisionByLabel`	Returns precision for each label (category).
`predictionCol`	Field in “predictions” which gives the prediction of each class.
`predictions`	Dataframe outputted by the model’s transform method.
`recallByLabel`	Returns recall for each label (category).
`totalIterations`	Number of training iterations until termination.
`truePositiveRateByLabel`	Returns true positive rate for each label (category).
`weightCol`	Field in “predictions” which gives the weight of each instance as a vector.
`weightedFalsePositiveRate`	Returns weighted false positive rate.
`weightedPrecision`	Returns weighted averaged precision.
`weightedRecall`	Returns weighted averaged recall.
`weightedTruePositiveRate`	Returns weighted true positive rate.

Methods Documentation

fMeasureByLabel(beta: float = 1.0) → List[float]¶: Returns f-measure for each label (category).

weightedFMeasure(beta: float = 1.0) → float¶: Returns weighted averaged f-measure.

Attributes Documentation

accuracy¶: Returns accuracy. (equals to the total number of correctly classified instances out of the total number of instances.)

falsePositiveRateByLabel¶: Returns false positive rate for each label (category).

labelCol¶: Field in “predictions” which gives the true label of each instance.

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.

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.

objectiveHistory¶: Objective function (scaled loss + regularization) at each iteration. It contains one more element, the initial state, than number of iterations.

precisionByLabel¶: Returns precision for each label (category).

predictionCol¶: Field in “predictions” which gives the prediction of each class.

predictions¶: Dataframe outputted by the model’s transform method.

recallByLabel¶: Returns recall for each label (category).

totalIterations¶: Number of training iterations until termination.

truePositiveRateByLabel¶: Returns true positive rate for each label (category).

weightCol¶: Field in “predictions” which gives the weight of each instance as a vector.

weightedFalsePositiveRate¶: Returns weighted false positive rate.

weightedPrecision¶: Returns weighted averaged precision.

weightedRecall¶: Returns weighted averaged recall. (equals to precision, recall and f-measure)

weightedTruePositiveRate¶: Returns weighted true positive rate. (equals to precision, recall and f-measure)

RandomForestClassificationSummary

BinaryRandomForestClassificationSummary