# 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.