LinearRegressionTrainingSummary¶
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class
pyspark.ml.regression.
LinearRegressionTrainingSummary
(java_obj=None)[source]¶ Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.
New in version 2.0.0.
Attributes
Attributes Documentation
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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
New in version 2.0.0.
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degreesOfFreedom
¶ Degrees of freedom.
New in version 2.2.0.
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devianceResiduals
¶ The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
New in version 2.0.0.
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explainedVariance
¶ Returns the explained variance regression score. explainedVariance = \(1 - \frac{variance(y - \hat{y})}{variance(y)}\)
See also
Note
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
New in version 2.0.0.
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featuresCol
¶ Field in “predictions” which gives the features of each instance as a vector.
New in version 2.0.0.
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labelCol
¶ Field in “predictions” which gives the true label of each instance.
New in version 2.0.0.
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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.
Note
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
New in version 2.0.0.
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meanSquaredError
¶ Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Note
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
New in version 2.0.0.
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numInstances
¶ Number of instances in DataFrame predictions
New in version 2.0.0.
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objectiveHistory
¶ Objective function (scaled loss + regularization) at each iteration. This value is only available when using the “l-bfgs” solver.
See also
New in version 2.0.0.
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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
New in version 2.0.0.
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predictionCol
¶ Field in “predictions” which gives the predicted value of the label at each instance.
New in version 2.0.0.
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predictions
¶ Dataframe outputted by the model’s transform method.
New in version 2.0.0.
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r2
¶ Returns R^2, the coefficient of determination.
Note
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
New in version 2.0.0.
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r2adj
¶ Returns Adjusted R^2, the adjusted coefficient of determination.
Note
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
New in version 2.4.0.
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residuals
¶ Residuals (label - predicted value)
New in version 2.0.0.
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rootMeanSquaredError
¶ Returns the root mean squared error, which is defined as the square root of the mean squared error.
Note
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
New in version 2.0.0.
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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
New in version 2.0.0.
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totalIterations
¶ Number of training iterations until termination. This value is only available when using the “l-bfgs” solver.
See also
New in version 2.0.0.
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