RegressionMetrics¶

class pyspark.mllib.evaluation.RegressionMetrics(predictionAndObservations: pyspark.rdd.RDD[Tuple[float, float]])¶

Evaluator for regression.

Parameters

predictionAndObservationspyspark.RDD: an RDD of prediction, observation and optional weight.

Examples

>>> predictionAndObservations = sc.parallelize([
...     (2.5, 3.0), (0.0, -0.5), (2.0, 2.0), (8.0, 7.0)])
>>> metrics = RegressionMetrics(predictionAndObservations)
>>> metrics.explainedVariance
8.859...
>>> metrics.meanAbsoluteError
0.5...
>>> metrics.meanSquaredError
0.37...
>>> metrics.rootMeanSquaredError
0.61...
>>> metrics.r2
0.94...
>>> predictionAndObservationsWithOptWeight = sc.parallelize([
...     (2.5, 3.0, 0.5), (0.0, -0.5, 1.0), (2.0, 2.0, 0.3), (8.0, 7.0, 0.9)])
>>> metrics = RegressionMetrics(predictionAndObservationsWithOptWeight)
>>> metrics.rootMeanSquaredError
0.68...

Methods

call(name, *a)

Call method of java_model

Attributes

`explainedVariance`	Returns the explained variance regression score.
`meanAbsoluteError`	Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
`meanSquaredError`	Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
`r2`	Returns R^2^, the coefficient of determination.
`rootMeanSquaredError`	Returns the root mean squared error, which is defined as the square root of the mean squared error.

Methods Documentation

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

Attributes Documentation

explainedVariance¶: Returns the explained variance regression score. explainedVariance = \(1 - \frac{variance(y - \hat{y})}{variance(y)}\)

meanAbsoluteError¶: Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.

meanSquaredError¶: Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.

r2¶: Returns R^2^, the coefficient of determination.

rootMeanSquaredError¶: Returns the root mean squared error, which is defined as the square root of the mean squared error.

BinaryClassificationMetrics

MulticlassMetrics