pyspark.ml.regression.
LinearRegressionTrainingSummary
Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.
New in version 2.0.0.
Attributes
coefficientStandardErrors
Standard error of estimated coefficients and intercept.
degreesOfFreedom
Degrees of freedom.
devianceResiduals
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
explainedVariance
Returns the explained variance regression score.
featuresCol
Field in “predictions” which gives the features of each instance as a vector.
labelCol
Field in “predictions” which gives the true label of each instance.
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.
numInstances
Number of instances in DataFrame predictions
objectiveHistory
Objective function (scaled loss + regularization) at each iteration.
pValues
Two-sided p-value of estimated coefficients and intercept.
predictionCol
Field in “predictions” which gives the predicted value of the label at each instance.
predictions
Dataframe outputted by the model’s transform method.
r2
Returns R^2, the coefficient of determination.
r2adj
Returns Adjusted R^2, the adjusted coefficient of determination.
residuals
Residuals (label - predicted value)
rootMeanSquaredError
Returns the root mean squared error, which is defined as the square root of the mean squared error.
tValues
T-statistic of estimated coefficients and intercept.
totalIterations
Number of training iterations until termination.
Attributes Documentation
Standard error of estimated coefficients and intercept. This value is only available when using the “normal” solver.
If LinearRegression.fitIntercept is set to True, then the last element returned corresponds to the intercept.
LinearRegression.fitIntercept
See also
LinearRegression.solver
New in version 2.2.0.
Returns the explained variance regression score. explainedVariance = \(1 - \frac{variance(y - \hat{y})}{variance(y)}\)
Notes
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
For additional information see Explained variation on Wikipedia
Objective function (scaled loss + regularization) at each iteration. This value is only available when using the “l-bfgs” solver.
Two-sided p-value of estimated coefficients and intercept. This value is only available when using the “normal” solver.
See also Wikipedia coefficient of determination
Wikipedia coefficient of determination, Adjusted R^2
New in version 2.4.0.
T-statistic of estimated coefficients and intercept. This value is only available when using the “normal” solver.
Number of training iterations until termination. This value is only available when using the “l-bfgs” solver.