# BinaryRandomForestClassificationSummary¶

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

BinaryRandomForestClassification 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. areaUnderROC Computes the area under the receiver operating characteristic (ROC) curve. fMeasureByThreshold Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0. 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. pr Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it. precisionByLabel Returns precision for each label (category). precisionByThreshold Returns a dataframe with two fields (threshold, precision) curve. 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). recallByThreshold Returns a dataframe with two fields (threshold, recall) curve. roc Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it. scoreCol Field in “predictions” which gives the probability or raw prediction of each class as a vector. 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.

areaUnderROC

Computes the area under the receiver operating characteristic (ROC) curve.

New in version 3.1.0.

fMeasureByThreshold

Returns a dataframe with two fields (threshold, F-Measure) curve with beta = 1.0.

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.

pr

Returns the precision-recall curve, which is a Dataframe containing two fields recall, precision with (0.0, 1.0) prepended to it.

New in version 3.1.0.

precisionByLabel

Returns precision for each label (category).

New in version 3.1.0.

precisionByThreshold

Returns a dataframe with two fields (threshold, precision) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision.

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.

recallByThreshold

Returns a dataframe with two fields (threshold, recall) curve. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the recall.

New in version 3.1.0.

roc

Returns the receiver operating characteristic (ROC) curve, which is a Dataframe having two fields (FPR, TPR) with (0.0, 0.0) prepended and (1.0, 1.0) appended to it.

New in version 3.1.0.

Notes

Wikipedia reference

scoreCol

Field in “predictions” which gives the probability or raw prediction of each class as a vector.

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.