DecisionTreeRegressionModel¶

class pyspark.ml.regression.DecisionTreeRegressionModel(java_model: Optional[JavaObject] = None)¶

Model fitted by DecisionTreeRegressor.

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	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 < user-supplied values < extra.
`getCacheNodeIds`()	Gets the value of cacheNodeIds or its default value.
`getCheckpointInterval`()	Gets the value of checkpointInterval or its default value.
`getFeaturesCol`()	Gets the value of featuresCol or its default value.
`getImpurity`()	Gets the value of impurity or its default value.
`getLabelCol`()	Gets the value of labelCol or its default value.
`getLeafCol`()	Gets the value of leafCol or its default value.
`getMaxBins`()	Gets the value of maxBins or its default value.
`getMaxDepth`()	Gets the value of maxDepth or its default value.
`getMaxMemoryInMB`()	Gets the value of maxMemoryInMB or its default value.
`getMinInfoGain`()	Gets the value of minInfoGain or its default value.
`getMinInstancesPerNode`()	Gets the value of minInstancesPerNode or its default value.
`getMinWeightFractionPerNode`()	Gets the value of minWeightFractionPerNode or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getParam`(paramName)	Gets a param by its name.
`getPredictionCol`()	Gets the value of predictionCol or its default value.
`getSeed`()	Gets the value of seed or its default value.
`getVarianceCol`()	Gets the value of varianceCol or its default value.
`getWeightCol`()	Gets the value of weightCol or its default value.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`predict`(value)	Predict label for the given features.
`predictLeaf`(value)	Predict the indices of the leaves corresponding to the feature vector.
`read`()	Returns an MLReader instance for this class.
`save`(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
`set`(param, value)	Sets a parameter in the embedded param map.
`setFeaturesCol`(value)	Sets the value of `featuresCol`.
`setLeafCol`(value)	Sets the value of `leafCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setVarianceCol`(value)	Sets the value of `varianceCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`cacheNodeIds`
`checkpointInterval`
`depth`	Return depth of the decision tree.
`featureImportances`	Estimate of the importance of each feature.
`featuresCol`
`impurity`
`labelCol`
`leafCol`
`maxBins`
`maxDepth`
`maxMemoryInMB`
`minInfoGain`
`minInstancesPerNode`
`minWeightFractionPerNode`
`numFeatures`	Returns the number of features the model was trained on.
`numNodes`	Return number of nodes of the decision tree.
`params`	Returns all params ordered by name.
`predictionCol`
`seed`
`supportedImpurities`
`toDebugString`	Full description of model.
`varianceCol`
`weightCol`

Methods Documentation

clear(param: pyspark.ml.param.Param) → None¶: Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP¶

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional: Extra parameters to copy to the new instance

Returns

JavaParams: Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str¶: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap¶

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 < user-supplied values < extra.

Parameters

extradict, optional: extra param values

Returns

dict: merged param map

getCacheNodeIds() → bool¶: Gets the value of cacheNodeIds or its default value.

getCheckpointInterval() → int¶: Gets the value of checkpointInterval or its default value.

getFeaturesCol() → str¶: Gets the value of featuresCol or its default value.

getImpurity() → str¶: Gets the value of impurity or its default value.

getLabelCol() → str¶: Gets the value of labelCol or its default value.

getLeafCol() → str¶: Gets the value of leafCol or its default value.

getMaxBins() → int¶: Gets the value of maxBins or its default value.

getMaxDepth() → int¶: Gets the value of maxDepth or its default value.

getMaxMemoryInMB() → int¶: Gets the value of maxMemoryInMB or its default value.

getMinInfoGain() → float¶: Gets the value of minInfoGain or its default value.

getMinInstancesPerNode() → int¶: Gets the value of minInstancesPerNode or its default value.

getMinWeightFractionPerNode() → float¶: Gets the value of minWeightFractionPerNode or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getPredictionCol() → str¶: Gets the value of predictionCol or its default value.

getSeed() → int¶: Gets the value of seed or its default value.

getVarianceCol() → str¶: Gets the value of varianceCol or its default value.

getWeightCol() → str¶: Gets the value of weightCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param has a default value.

hasParam(paramName: str) → bool¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL¶: Reads an ML instance from the input path, a shortcut of read().load(path).

predict(value: T) → float¶: Predict label for the given features.

predictLeaf(value: pyspark.ml.linalg.Vector) → float¶: Predict the indices of the leaves corresponding to the feature vector.

classmethod read() → pyspark.ml.util.JavaMLReader[RL]¶: Returns an MLReader instance for this class.

save(path: str) → None¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶: Sets a parameter in the embedded param map.

setFeaturesCol(value: str) → P¶: Sets the value of featuresCol.

setLeafCol(value: str) → P¶: Sets the value of leafCol.

setPredictionCol(value: str) → P¶: Sets the value of predictionCol.

setVarianceCol(value: str) → pyspark.ml.regression.DecisionTreeRegressionModel ¶: Sets the value of varianceCol.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

Transforms the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

cacheNodeIds = Param(parent='undefined', name='cacheNodeIds', doc='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.')¶

checkpointInterval = Param(parent='undefined', name='checkpointInterval', doc='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.')¶

depth¶: Return depth of the decision tree.

featureImportances¶

Estimate of the importance of each feature.

This generalizes the idea of “Gini” importance to other losses, following the explanation of Gini importance from “Random Forests” documentation by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.

This feature importance is calculated as follows:

importance(feature j) = sum (over nodes which split on feature j) of the gain, where gain is scaled by the number of instances passing through node
Normalize importances for tree to sum to 1.

Notes

Feature importance for single decision trees can have high variance due to correlated predictor variables. Consider using a RandomForestRegressor to determine feature importance instead.

featuresCol = Param(parent='undefined', name='featuresCol', doc='features column name.')¶

impurity = Param(parent='undefined', name='impurity', doc='Criterion used for information gain calculation (case-insensitive). Supported options: variance')¶

labelCol: Param[str] = Param(parent='undefined', name='labelCol', doc='label column name.')¶

leafCol = Param(parent='undefined', name='leafCol', doc='Leaf indices column name. Predicted leaf index of each instance in each tree by preorder.')¶

maxBins = Param(parent='undefined', name='maxBins', doc='Max number of bins for discretizing continuous features. Must be >=2 and >= number of categories for any categorical feature.')¶

maxDepth = Param(parent='undefined', name='maxDepth', doc='Maximum depth of the tree. (>= 0) E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. Must be in range [0, 30].')¶

maxMemoryInMB = Param(parent='undefined', name='maxMemoryInMB', doc='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.')¶

minInfoGain = Param(parent='undefined', name='minInfoGain', doc='Minimum information gain for a split to be considered at a tree node.')¶

minInstancesPerNode = Param(parent='undefined', name='minInstancesPerNode', doc='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. Should be >= 1.')¶

minWeightFractionPerNode = Param(parent='undefined', name='minWeightFractionPerNode', doc='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 interval [0.0, 0.5).')¶

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

numNodes¶: Return number of nodes of the decision tree.

params¶: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶

seed = Param(parent='undefined', name='seed', doc='random seed.')¶

supportedImpurities = ['variance']¶

toDebugString¶: Full description of model.

varianceCol = Param(parent='undefined', name='varianceCol', doc='column name for the biased sample variance of prediction.')¶

weightCol = Param(parent='undefined', name='weightCol', doc='weight column name. If this is not set or empty, we treat all instance weights as 1.0.')¶

DecisionTreeRegressor

GBTRegressor