LinearSVCTrainingSummary

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

Abstraction for LinearSVC Training results.

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.

objectiveHistory

Objective function (scaled loss + regularization) at each iteration.

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.

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).

New in version 3.1.0.

weightedFMeasure(beta: float = 1.0) → float

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.

objectiveHistory

Objective function (scaled loss + regularization) at each iteration. It contains one more element, the initial state, than number of iterations.

New in version 3.1.0.

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.

totalIterations

Number of training iterations until termination.

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.