RandomForestClassificationTrainingSummary#

class pyspark.ml.classification.RandomForestClassificationTrainingSummary(java_obj=None)[source]#

Abstraction for RandomForestClassificationTraining Training results.

Added in version 3.1.0.

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=1.0)#: Returns f-measure for each label (category).

Added in version 3.1.0.

weightedFMeasure(beta=1.0)#: Returns weighted averaged f-measure.

Added in version 3.1.0.

Attributes Documentation

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

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

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.

Added 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.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

totalIterations#: Number of training iterations until termination.

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

weightedFalsePositiveRate#: Returns weighted false positive rate.

Added in version 3.1.0.

weightedPrecision#: Returns weighted averaged precision.

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.