An alias for getOrDefault().

Param for suggested depth for treeAggregate (>= 2).

Clears the user-supplied value for the input param.

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Copies param values from this instance to another instance for params shared by them.

This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.

The shape parameter to control the amount of robustness. Must be > 1.0. At larger values of epsilon, the huber criterion becomes more similar to least squares regression; for small values of epsilon, the criterion is more similar to L1 regression. Default is 1.35 to get as much robustness as possible while retaining 95% statistical efficiency for normally distributed data. It matches sklearn HuberRegressor and is "M" from A robust hybrid of lasso and ridge regression. Only valid when "loss" is "huber".

Evaluates the model on a test dataset.

Explains a param.

Explains all params of this instance. See explainParam().

extractParamMap with no extra values.

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

Param for features column name.

Returns the SQL DataType corresponding to the FeaturesType type parameter.

This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

Param for whether to fit an intercept term.

Optionally returns the user-supplied value of a param.

Gets the default value of a parameter.

Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

Gets a param by its name.

Tests whether the input param has a default value set.

Tests whether this instance contains a param with a given name.

Indicates whether this Model has a corresponding parent.

Indicates whether a training summary exists for this model instance.

Checks whether a param is explicitly set or has a default value.

Checks whether a param is explicitly set.

Param for label column name.

The loss function to be optimized. Supported options: "squaredError" and "huber". Default: "squaredError"

Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..

Param for maximum number of iterations (>= 0).

Returns the number of features the model was trained on. If unknown, returns -1

Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

The parent estimator that produced this model.

Predict label for the given features. This method is used to implement transform() and output predictionCol.

Param for prediction column name.

Param for regularization parameter (>= 0).

Saves this ML instance to the input path, a shortcut of write.save(path).

Sets a parameter in the embedded param map.

Sets a parameter (by name) in the embedded param map.

Sets a parameter in the embedded param map.

Sets default values for a list of params.

Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

Sets a default value for a param.

Sets the parent of this model (Java API).

The solver algorithm for optimization. Supported options: "l-bfgs", "normal" and "auto". Default: "auto"

Param for whether to standardize the training features before fitting the model.

Gets summary (e.g. residuals, mse, r-squared ) of model on training set. An exception is thrown if hasSummary is false.

Param for the convergence tolerance for iterative algorithms (>= 0).

Transforms dataset by reading from featuresCol, calling predict, and storing the predictions as a new column predictionCol.

Transforms the dataset with provided parameter map as additional parameters.

Transforms the dataset with optional parameters

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

Derives the output schema from the input schema and parameters, optionally with logging.

This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

An immutable unique ID for the object and its derivatives.

Validates and transforms the input schema with the provided param map.

Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

Returns a org.apache.spark.ml.util.GeneralMLWriter instance for this ML instance.

For LinearRegressionModel, this does NOT currently save the training summary. An option to save summary may be added in the future.

This also does not save the parent currently.