BinaryLogisticRegressionSummary¶

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

Binary Logistic regression 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.
`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).
`featuresCol`	Field in “predictions” which gives the features of each instance as a vector.
`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.
`probabilityCol`	Field in “predictions” which gives the probability of each class as a vector.
`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: 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.)

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).

featuresCol¶: Field in “predictions” which gives the features of each instance as a vector.

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.

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. Every possible probability obtained in transforming the dataset are used as thresholds used in calculating the precision.

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

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

probabilityCol¶: Field in “predictions” which gives the probability of each class as a vector.

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

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.

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.

Notes

Wikipedia reference

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

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

LogisticRegressionTrainingSummary

BinaryLogisticRegressionTrainingSummary