LinearRegressionTrainingSummary¶

class pyspark.ml.regression.LinearRegressionTrainingSummary(java_obj: Optional[JavaObject] = None)¶

Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.

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

coefficientStandardErrors¶

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.

See also

LinearRegression.solver

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. 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

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.

Notes

This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

meanSquaredError¶

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

Notes

This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

numInstances¶: Number of instances in DataFrame predictions

objectiveHistory¶

Objective function (scaled loss + regularization) at each iteration. This value is only available when using the “l-bfgs” solver.

See also

LinearRegression.solver

pValues¶

Two-sided p-value 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.

See also

LinearRegression.solver

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.

Notes

This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

r2adj¶

Returns Adjusted R^2, the adjusted coefficient of determination.

Notes

This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

Wikipedia coefficient of determination, Adjusted R^2

residuals¶: Residuals (label - predicted value)

rootMeanSquaredError¶

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

Notes

This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.

tValues¶

T-statistic 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.

See also

LinearRegression.solver

totalIterations¶

Number of training iterations until termination. This value is only available when using the “l-bfgs” solver.

See also

LinearRegression.solver

LinearRegressionSummary

RandomForestRegressor