class LinearRegressionTrainingSummary extends LinearRegressionSummary
Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.
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- @Since("1.5.0")
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- LinearRegression.scala
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- lazy val coefficientStandardErrors: Array[Double]
Standard error of estimated coefficients and intercept.
Standard error of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If
LinearRegression.fitInterceptis set to true, then the last element returned corresponds to the intercept.- Definition Classes
- LinearRegressionSummary
- See also
LinearRegression.solver
- val degreesOfFreedom: Long
Degrees of freedom
Degrees of freedom
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- LinearRegressionSummary
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- @Since("2.2.0")
- lazy val devianceResiduals: Array[Double]
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
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- LinearRegressionSummary
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- val explainedVariance: Double
Returns the explained variance regression score.
Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation
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- LinearRegressionSummary
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- @Since("1.5.0")
- val featuresCol: String
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- LinearRegressionSummary
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- val labelCol: String
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- 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.
Returns the mean absolute error, which is a risk function corresponding to the expected value of the absolute error loss or l1-norm loss.
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- LinearRegressionSummary
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- @Since("1.5.0")
- 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.
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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- LinearRegressionSummary
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- lazy val numInstances: Long
Number of instances in DataFrame predictions
Number of instances in DataFrame predictions
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- LinearRegressionSummary
- val objectiveHistory: Array[Double]
- lazy val pValues: Array[Double]
Two-sided p-value of estimated coefficients and intercept.
Two-sided p-value of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If
LinearRegression.fitInterceptis set to true, then the last element returned corresponds to the intercept.- Definition Classes
- LinearRegressionSummary
- See also
LinearRegression.solver
- val predictionCol: String
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- LinearRegressionSummary
- val predictions: DataFrame
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- LinearRegressionSummary
- val r2: Double
Returns R2, the coefficient of determination.
Returns R2, the coefficient of determination. Reference: Wikipedia coefficient of determination
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- LinearRegressionSummary
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- @Since("1.5.0")
- val r2adj: Double
Returns Adjusted R2, the adjusted coefficient of determination.
Returns Adjusted R2, the adjusted coefficient of determination. Reference: Wikipedia coefficient of determination
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- LinearRegressionSummary
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- @Since("2.3.0")
- lazy val residuals: DataFrame
Residuals (label - predicted value)
Residuals (label - predicted value)
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- LinearRegressionSummary
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- val rootMeanSquaredError: Double
Returns the root mean squared error, which is defined as the square root of the mean squared error.
Returns the root mean squared error, which is defined as the square root of the mean squared error.
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- LinearRegressionSummary
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- lazy val tValues: Array[Double]
T-statistic of estimated coefficients and intercept.
T-statistic of estimated coefficients and intercept. This value is only available when using the "normal" solver.
If
LinearRegression.fitInterceptis set to true, then the last element returned corresponds to the intercept.- Definition Classes
- LinearRegressionSummary
- See also
LinearRegression.solver
- def toString(): String
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- val totalIterations: Int
Number of training iterations until termination
Number of training iterations until termination
This value is only available when using the "l-bfgs" solver.
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- @Since("1.5.0")
- See also
LinearRegression.solver
- final def wait(arg0: Long, arg1: Int): Unit
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(Since version 9)