LinearRegressionSummary¶
-
class
pyspark.ml.regression.LinearRegressionSummary(java_obj: Optional[JavaObject] = None)¶ Linear regression results evaluated on a dataset.
Attributes
Standard error of estimated coefficients and intercept.
Degrees of freedom.
The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
Returns the explained variance regression score.
Field in “predictions” which gives the features of each instance as a vector.
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 squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Number of instances in DataFrame predictions
Two-sided p-value of estimated coefficients and intercept.
Field in “predictions” which gives the predicted value of the label at each instance.
Dataframe outputted by the model’s transform method.
Returns R^2, the coefficient of determination.
Returns Adjusted R^2, the adjusted coefficient of determination.
Residuals (label - predicted value)
Returns the root mean squared error, which is defined as the square root of the mean squared error.
T-statistic of estimated coefficients and intercept.
Attributes Documentation
-
coefficientStandardErrors¶ 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.See also
-
degreesOfFreedom¶ Degrees of freedom.
-
devianceResiduals¶ The weighted residuals, the usual residuals rescaled by the square root of the instance weights.
-
explainedVariance¶ Returns the explained variance regression score. explainedVariance = \(1 - \frac{variance(y - \hat{y})}{variance(y)}\)
Notes
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
For additional information see Explained variation on Wikipedia
-
featuresCol¶ Field in “predictions” which gives the features of each instance as a vector.
-
labelCol¶ Field in “predictions” which gives the true label of each instance.
-
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.
Notes
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
-
meanSquaredError¶ Returns the mean squared error, which is a risk function corresponding to the expected value of the squared error loss or quadratic loss.
Notes
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
-
numInstances¶ Number of instances in DataFrame predictions
-
pValues¶ 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.See also
-
predictionCol¶ Field in “predictions” which gives the predicted value of the label at each instance.
-
predictions¶ Dataframe outputted by the model’s transform method.
-
r2¶ Returns R^2, the coefficient of determination.
Notes
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
-
r2adj¶ Returns Adjusted R^2, the adjusted coefficient of determination.
Notes
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
-
residuals¶ Residuals (label - predicted value)
-
rootMeanSquaredError¶ Returns the root mean squared error, which is defined as the square root of the mean squared error.
Notes
This ignores instance weights (setting all to 1.0) from LinearRegression.weightCol. This will change in later Spark versions.
-
tValues¶ 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.See also
-