org.apache.spark.ml.regression

LinearRegressionSummary

class LinearRegressionSummary extends Serializable

:: Experimental :: Linear regression results evaluated on a dataset.

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@Experimental()
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  10. val explainedVariance: Double

    Returns the explained variance regression score.

    Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: http://en.wikipedia.org/wiki/Explained_variation

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  15. val labelCol: String

  16. val meanAbsoluteError: Double

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

  17. val meanSquaredError: Double

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

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  21. val predictionCol: String

  22. val predictions: DataFrame

    predictions outputted by the model's transform method.

  23. val r2: Double

    Returns R2, the coefficient of determination.

    Returns R2, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination

  24. lazy val residuals: DataFrame

    Residuals (label - predicted value)

  25. val rootMeanSquaredError: Double

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

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