org.apache.spark.ml.regression
LinearRegressionTrainingSummary
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package regression
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
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.
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.
objective function (scaled loss + regularization) at each iteration.
predictions outputted by the model's transform method.
predictions outputted by the model's transform method.
Returns R2, the coefficient of determination.
Returns R2, the coefficient of determination. Reference: http://en.wikipedia.org/wiki/Coefficient_of_determination
Residuals (label - predicted value)
Residuals (label - predicted value)
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.
Number of training iterations until termination
:: Experimental :: Linear regression training results.