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".

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.

Fits a model to the input data.

Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

Fits a single model to the input data with provided parameter map.

Fits a single model to the input data with optional parameters.

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.

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

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.

Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.

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.

Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.

Note: Fitting with huber loss only supports None and L2 regularization, so throws exception if this param is non-zero value.

Sets the value of param epsilon. Default is 1.35.

Set if we should fit the intercept. Default is true.

Sets the value of param loss. Default is "squaredError".

Sets the value of param maxBlockSizeInMB. Default is 0.0, then 1.0 MB will be chosen.

Set the maximum number of iterations. Default is 100.

Set the regularization parameter. Default is 0.0.

Set the solver algorithm used for optimization. In case of linear regression, this can be "l-bfgs", "normal" and "auto".

• "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton optimization method.
• "normal" denotes using Normal Equation as an analytical solution to the linear regression problem. This solver is limited to LinearRegression.MAX_FEATURES_FOR_NORMAL_SOLVER.
• "auto" (default) means that the solver algorithm is selected automatically. The Normal Equations solver will be used when possible, but this will automatically fall back to iterative optimization methods when needed.

Note: Fitting with huber loss doesn't support normal solver, so throws exception if this param was set with "normal".

Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Default is true.

Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy with the cost of more iterations. Default is 1E-6.

Whether to over-/under-sample training instances according to the given weights in weightCol. If not set or empty, all instances are treated equally (weight 1.0). Default is not set, so all instances have weight one.

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.

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

Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

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.

:: DeveloperApi ::

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 an MLWriter instance for this ML instance.