FMClassificationSummary#

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

Abstraction for FMClassifier Results for a given model.

Added 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).

Added in version 3.1.0.

weightedFMeasure(beta=1.0)#: Returns weighted averaged f-measure.

Added 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.)

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

falsePositiveRateByLabel#: Returns false positive rate for each label (category).

Added in version 3.1.0.

labelCol#: Field in “predictions” which gives the true label of each instance.

Added 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.

Added 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.

Added in version 3.1.0.

precisionByLabel#: Returns precision for each label (category).

Added 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.

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added in version 3.1.0.

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

Added 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.

Added 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.

Added 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.

Added in version 3.1.0.

truePositiveRateByLabel#: Returns true positive rate for each label (category).

Added in version 3.1.0.

weightCol#: Field in “predictions” which gives the weight of each instance as a vector.

Added in version 3.1.0.

weightedFalsePositiveRate#: Returns weighted false positive rate.

Added in version 3.1.0.

weightedPrecision#: Returns weighted averaged precision.

Added in version 3.1.0.

weightedRecall#: Returns weighted averaged recall. (equals to precision, recall and f-measure)

Added in version 3.1.0.

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

Added in version 3.1.0.