# RandomForestClassificationSummary¶

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

Abstraction for RandomForestClassification Results for a given model.

New 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. 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). 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).

New in version 3.1.0.

weightedFMeasure(beta=1.0)

Returns weighted averaged f-measure.

New 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.)

New in version 3.1.0.

falsePositiveRateByLabel

Returns false positive rate for each label (category).

New in version 3.1.0.

labelCol

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

New 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.

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.

precisionByLabel

Returns precision for each label (category).

New in version 3.1.0.

predictionCol

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

New in version 3.1.0.

predictions

Dataframe outputted by the model’s transform method.

New in version 3.1.0.

recallByLabel

Returns recall for each label (category).

New in version 3.1.0.

truePositiveRateByLabel

Returns true positive rate for each label (category).

New in version 3.1.0.

weightCol

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

New in version 3.1.0.

weightedFalsePositiveRate

Returns weighted false positive rate.

New in version 3.1.0.

weightedPrecision

Returns weighted averaged precision.

New in version 3.1.0.

weightedRecall

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

New in version 3.1.0.

weightedTruePositiveRate

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

New in version 3.1.0.