An alias for getOrDefault().

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

Gets summary of model on training set. An exception is thrown if hasSummary is false or it is a multiclass model.

If threshold and thresholds are both set, ensures they are consistent.

Clears the user-supplied value for the input param.

A vector of model coefficients for "binomial" logistic regression. If this model was trained using the "multinomial" family then an exception is thrown.

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.

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 the name of family which is a description of the label distribution to be used in the model. Supported options:

• "auto": Automatically select the family based on the number of classes: If numClasses == 1 || numClasses == 2, set to "binomial". Else, set to "multinomial"
• "binomial": Binary logistic regression with pivoting.
• "multinomial": Multinomial logistic (softmax) regression without pivoting. Default is "auto".

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.

Get threshold for binary classification.

If thresholds is set with length 2 (i.e., binary classification), this returns the equivalent threshold:

1 / (1 + thresholds(0) / thresholds(1))

. Otherwise, returns threshold if set, or its default value if unset.

1 / (1 + thresholds(0) / thresholds(1)) }}} Otherwise, returns threshold if set, or its default value if unset.

Get thresholds for binary or multiclass classification.

If thresholds is set, return its value. Otherwise, if threshold is set, return the equivalent thresholds for binary classification: (1-threshold, threshold). If neither are set, throw an exception.

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.

The model intercept for "binomial" logistic regression. If this model was fit with the "multinomial" family then an exception is thrown.

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 lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

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).

Number of classes (values which the label can take).

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 feature vector. The behavior of this can be adjusted using thresholds.

Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

This internal method is used to implement transform() and output probabilityCol.

Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

Param for prediction column name.

Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.

Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.

Non-in-place version of raw2probabilityInPlace()

Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

This internal method is used to implement transform() and output probabilityCol.

Param for raw prediction (a.k.a. confidence) 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).

Set threshold in binary classification, in range [0, 1].

If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.

Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)). When setThreshold() is called, any user-set value for thresholds will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

Default is 0.5.

Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

Note: When setThresholds() is called, any user-set value for threshold will be cleared. If both threshold and thresholds are set in a ParamMap, then they must be equivalent.

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

Gets summary of model on training set. An exception is thrown if hasSummary is false.

Param for threshold in binary classification prediction, in range [0, 1].

Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

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

Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

• predicted labels as predictionCol of type Double
• raw predictions (confidences) as rawPredictionCol of type Vector
• probability of each class as probabilityCol of type Vector.

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.

:: 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.

The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.

The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.

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.MLWriter instance for this ML instance.

For LogisticRegressionModel, 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.