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.fitIntercept
is set to true,
then the last element returned corresponds to the intercept.
LinearRegression.solver
Degrees of freedom
Degrees of freedom
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.
Returns the explained variance regression score.
Returns the explained variance regression score. explainedVariance = 1 - variance(y - \hat{y}) / variance(y) Reference: Wikipedia explain variation
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
Field in "predictions" which gives the features of each instance as a vector.
Field in "predictions" which gives the features of each instance as a vector.
Field in "predictions" which gives the true label of each instance.
Field in "predictions" which gives the true label of each instance.
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.
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
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.
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
Number of instances in DataFrame predictions
Number of instances in DataFrame predictions
objective function (scaled loss + regularization) at each iteration.
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.fitIntercept
is set to true,
then the last element returned corresponds to the intercept.
LinearRegression.solver
Field in "predictions" which gives the predicted value of the label at each instance.
Field in "predictions" which gives the predicted value of the label at each instance.
predictions output by the model's transform
method.
predictions output by the model's transform
method.
Returns R^{2}, the coefficient of determination.
Returns R^{2}, the coefficient of determination. Reference: Wikipedia coefficient of determination
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
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.
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol
.
This will change in later Spark versions.
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.fitIntercept
is set to true,
then the last element returned corresponds to the intercept.
LinearRegression.solver
Number of training iterations until termination
Number of training iterations until termination
This value is only available when using the "l-bfgs" solver.
LinearRegression.solver
:: Experimental :: Linear regression training results. Currently, the training summary ignores the training weights except for the objective trace.