RandomForestClassificationSummary

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

Abstraction for RandomForestClassification Results for a given model.

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: 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.

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

weightedTruePositiveRate

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