BinaryClassificationMetrics¶

class pyspark.mllib.evaluation.BinaryClassificationMetrics(scoreAndLabels: pyspark.rdd.RDD[Tuple[float, float]])[source]¶

Evaluator for binary classification.

New in version 1.4.0.

Parameters

scoreAndLabelspyspark.RDD: an RDD of score, label and optional weight.

Examples

>>> scoreAndLabels = sc.parallelize([
...     (0.1, 0.0), (0.1, 1.0), (0.4, 0.0), (0.6, 0.0), (0.6, 1.0), (0.6, 1.0), (0.8, 1.0)], 2)
>>> metrics = BinaryClassificationMetrics(scoreAndLabels)
>>> metrics.areaUnderROC
0.70...
>>> metrics.areaUnderPR
0.83...
>>> metrics.unpersist()
>>> scoreAndLabelsWithOptWeight = sc.parallelize([
...     (0.1, 0.0, 1.0), (0.1, 1.0, 0.4), (0.4, 0.0, 0.2), (0.6, 0.0, 0.6), (0.6, 1.0, 0.9),
...     (0.6, 1.0, 0.5), (0.8, 1.0, 0.7)], 2)
>>> metrics = BinaryClassificationMetrics(scoreAndLabelsWithOptWeight)
>>> metrics.areaUnderROC
0.79...
>>> metrics.areaUnderPR
0.88...

Methods

`call`(name, *a)	Call method of java_model
`unpersist`()	Unpersists intermediate RDDs used in the computation.

Attributes

`areaUnderPR`	Computes the area under the precision-recall curve.
`areaUnderROC`	Computes the area under the receiver operating characteristic (ROC) curve.

Methods Documentation

call(name: str, *a: Any) → Any¶: Call method of java_model

unpersist() → None[source]¶: Unpersists intermediate RDDs used in the computation.

New in version 1.4.0.

Attributes Documentation

areaUnderPR¶: Computes the area under the precision-recall curve.

New in version 1.4.0.

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

New in version 1.4.0.

LDAModel

RegressionMetrics