RandomForestClassificationModel

class pyspark.ml.classification.RandomForestClassificationModel(java_model: Optional[JavaObject] = None)

Model fitted by RandomForestClassifier.

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.
evaluate(dataset)	Evaluates the model on a test dataset.
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.
getBootstrap()	Gets the value of bootstrap or its default value.
getCacheNodeIds()	Gets the value of cacheNodeIds or its default value.
getCheckpointInterval()	Gets the value of checkpointInterval or its default value.
getFeatureSubsetStrategy()	Gets the value of featureSubsetStrategy 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.
getProbabilityCol()	Gets the value of probabilityCol or its default value.
getRawPredictionCol()	Gets the value of rawPredictionCol or its default value.
getSeed()	Gets the value of seed or its default value.
getSubsamplingRate()	Gets the value of subsamplingRate or its default value.
getThresholds()	Gets the value of thresholds 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.
predictProbability(value)	Predict the probability of each class given the features.
predictRaw(value)	Raw prediction for each possible label.
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.
setProbabilityCol(value)	Sets the value of probabilityCol.
setRawPredictionCol(value)	Sets the value of rawPredictionCol.
setThresholds(value)	Sets the value of thresholds.
transform(dataset[, params])	Transforms the input dataset with optional parameters.
write()	Returns an MLWriter instance for this ML instance.

Attributes

bootstrap
cacheNodeIds
checkpointInterval
featureImportances	Estimate of the importance of each feature.
featureSubsetStrategy
featuresCol
getNumTrees	Number of trees in ensemble.
hasSummary	Indicates whether a training summary exists for this model instance.
impurity
labelCol
leafCol
maxBins
maxDepth
maxMemoryInMB
minInfoGain
minInstancesPerNode
minWeightFractionPerNode
numClasses	Number of classes (values which the label can take).
numFeatures	Returns the number of features the model was trained on.
numTrees
params	Returns all params ordered by name.
predictionCol
probabilityCol
rawPredictionCol
seed
subsamplingRate
summary	Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set.
supportedFeatureSubsetStrategies
supportedImpurities
thresholds
toDebugString	Full description of model.
totalNumNodes	Total number of nodes, summed over all trees in the ensemble.
treeWeights	Return the weights for each tree
trees	Trees in this ensemble.
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

evaluate(dataset: pyspark.sql.dataframe.DataFrame) → Union[pyspark.ml.classification.BinaryRandomForestClassificationSummary, pyspark.ml.classification.RandomForestClassificationSummary]

Evaluates the model on a test dataset.

Parameters

datasetpyspark.sql.DataFrame

Test dataset to evaluate model on.

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

getBootstrap() → bool

Gets the value of bootstrap or its default value.

getCacheNodeIds() → bool

Gets the value of cacheNodeIds or its default value.

getCheckpointInterval() → int

Gets the value of checkpointInterval or its default value.

getFeatureSubsetStrategy() → str

Gets the value of featureSubsetStrategy 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.

getProbabilityCol() → str

Gets the value of probabilityCol or its default value.

getRawPredictionCol() → str

Gets the value of rawPredictionCol or its default value.

getSeed() → int

Gets the value of seed or its default value.

getSubsamplingRate() → float

Gets the value of subsamplingRate or its default value.

getThresholds() → List[float]

Gets the value of thresholds 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.

predictProbability(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector

Predict the probability of each class given the features.

predictRaw(value: pyspark.ml.linalg.Vector) → pyspark.ml.linalg.Vector

Raw prediction for each possible label.

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.

setProbabilityCol(value: str) → CM

Sets the value of probabilityCol.

setRawPredictionCol(value: str) → P

Sets the value of rawPredictionCol.

setThresholds(value: List[float]) → CM

Sets the value of thresholds.

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

bootstrap = Param(parent='undefined', name='bootstrap', doc='Whether bootstrap samples are used when building trees.')

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.')

featureImportances

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 also

DecisionTreeClassificationModel.featureImportances

featureSubsetStrategy = Param(parent='undefined', name='featureSubsetStrategy', doc="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 > 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'")

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

getNumTrees

Number of trees in ensemble.

hasSummary

Indicates whether a training summary exists for this model instance.

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

labelCol = 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).')

numClasses

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

numFeatures

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

numTrees = Param(parent='undefined', name='numTrees', doc='Number of trees to train (>= 1).')

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.')

probabilityCol: Param[str] = Param(parent='undefined', name='probabilityCol', doc='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.')

rawPredictionCol = Param(parent='undefined', name='rawPredictionCol', doc='raw prediction (a.k.a. confidence) column name.')

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

subsamplingRate = Param(parent='undefined', name='subsamplingRate', doc='Fraction of the training data used for learning each decision tree, in range (0, 1].')

summary

Gets summary (accuracy/precision/recall, objective history, total iterations) of model trained on the training set. An exception is thrown if trainingSummary is None.

supportedFeatureSubsetStrategies = ['auto', 'all', 'onethird', 'sqrt', 'log2']

supportedImpurities = ['entropy', 'gini']

thresholds = Param(parent='undefined', name='thresholds', doc="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.")

toDebugString

Full description of model.

totalNumNodes

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

treeWeights

Return the weights for each tree

trees

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

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.')

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