# 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)