An alias for getOrDefault().

If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default = false)

Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.

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.

Method to compute error or loss for every iteration of gradient boosting.

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.

Estimate of the importance of each feature.

Each feature's importance is the average of its importance across all trees in the ensemble The importance vector is normalized to sum to 1. This method is suggested by Hastie et al. (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.) and follows the implementation from scikit-learn.

See DecisionTreeClassificationModel.featureImportances

The number of features to consider for splits at each tree node. Supported options:

• "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees greater than 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
• "all": use all features
• "onethird": use 1/3 of the features
• "sqrt": use sqrt(number of features)
• "log2": use log2(number of features)
• "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

These various settings are based on the following references:

• log2: tested in Breiman (2001)
• sqrt: recommended by Breiman manual for random forests
• The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.

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.

Optionally returns the user-supplied value of a param.

Gets the default value of a parameter.

Number of trees in ensemble

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.

Criterion used for information gain calculation (case-insensitive). This impurity type is used in DecisionTreeRegressor, RandomForestRegressor, GBTRegressor and GBTClassifier (since GBTClassificationModel is internally composed of DecisionTreeRegressionModels). Supported: "variance". (default = variance)

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

Checks whether a param is explicitly set.

Param for label column name.

Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")

Loss function which GBT tries to minimize. (case-insensitive) Supported: "logistic" (default = logistic)

Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be at least 2 and at least number of categories in any categorical feature. (default = 32)

Maximum depth of the tree (nonnegative). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)

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

Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default = 256 MB)

Minimum information gain for a split to be considered at a tree node. Should be at least 0.0. (default = 0.0)

Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Must be at least 1. (default = 1)

Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in the interval [0.0, 0.5). (default = 0.0)

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 features. This method is used to implement transform() and output predictionCol.

This default implementation for classification predicts the index of the maximum value from predictRaw().

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.

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

Param for random seed.

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).

Param for Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default = 0.1)

Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

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.

Full description of model

Summary of the model

Total number of nodes, summed over all trees in the ensemble.

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.

Weights for each tree, zippable with trees

Trees in this ensemble. Warning: These have null parent Estimators.

An immutable unique ID for the object and its derivatives.

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

Param for name of the column that indicates whether each row is for training or for validation. False indicates training; true indicates validation..

Threshold for stopping early when fit with validation is used. (This parameter is ignored when fit without validation is used.) The decision to stop early is decided based on this logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01.

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.