pyspark.ml.classification.
DecisionTreeClassificationModel
Model fitted by DecisionTreeClassifier.
New in version 1.4.0.
Methods
clear(param)
clear
Clears a param from the param map if it has been explicitly set.
copy([extra])
copy
Creates a copy of this instance with the same uid and some extra params.
explainParam(param)
explainParam
Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
explainParams()
explainParams
Returns the documentation of all params with their optionally default values and user-supplied values.
extractParamMap([extra])
extractParamMap
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()
getCacheNodeIds
Gets the value of cacheNodeIds or its default value.
getCheckpointInterval()
getCheckpointInterval
Gets the value of checkpointInterval or its default value.
getFeaturesCol()
getFeaturesCol
Gets the value of featuresCol or its default value.
getImpurity()
getImpurity
Gets the value of impurity or its default value.
getLabelCol()
getLabelCol
Gets the value of labelCol or its default value.
getLeafCol()
getLeafCol
Gets the value of leafCol or its default value.
getMaxBins()
getMaxBins
Gets the value of maxBins or its default value.
getMaxDepth()
getMaxDepth
Gets the value of maxDepth or its default value.
getMaxMemoryInMB()
getMaxMemoryInMB
Gets the value of maxMemoryInMB or its default value.
getMinInfoGain()
getMinInfoGain
Gets the value of minInfoGain or its default value.
getMinInstancesPerNode()
getMinInstancesPerNode
Gets the value of minInstancesPerNode or its default value.
getMinWeightFractionPerNode()
getMinWeightFractionPerNode
Gets the value of minWeightFractionPerNode or its default value.
getOrDefault(param)
getOrDefault
Gets the value of a param in the user-supplied param map or its default value.
getParam(paramName)
getParam
Gets a param by its name.
getPredictionCol()
getPredictionCol
Gets the value of predictionCol or its default value.
getProbabilityCol()
getProbabilityCol
Gets the value of probabilityCol or its default value.
getRawPredictionCol()
getRawPredictionCol
Gets the value of rawPredictionCol or its default value.
getSeed()
getSeed
Gets the value of seed or its default value.
getThresholds()
getThresholds
Gets the value of thresholds or its default value.
getWeightCol()
getWeightCol
Gets the value of weightCol or its default value.
hasDefault(param)
hasDefault
Checks whether a param has a default value.
hasParam(paramName)
hasParam
Tests whether this instance contains a param with a given (string) name.
isDefined(param)
isDefined
Checks whether a param is explicitly set by user or has a default value.
isSet(param)
isSet
Checks whether a param is explicitly set by user.
load(path)
load
Reads an ML instance from the input path, a shortcut of read().load(path).
predict(value)
predict
Predict label for the given features.
predictLeaf(value)
predictLeaf
Predict the indices of the leaves corresponding to the feature vector.
predictProbability(value)
predictProbability
Predict the probability of each class given the features.
predictRaw(value)
predictRaw
Raw prediction for each possible label.
read()
read
Returns an MLReader instance for this class.
save(path)
save
Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
set(param, value)
set
Sets a parameter in the embedded param map.
setFeaturesCol(value)
setFeaturesCol
Sets the value of featuresCol.
featuresCol
setLeafCol(value)
setLeafCol
Sets the value of leafCol.
leafCol
setPredictionCol(value)
setPredictionCol
Sets the value of predictionCol.
predictionCol
setProbabilityCol(value)
setProbabilityCol
Sets the value of probabilityCol.
probabilityCol
setRawPredictionCol(value)
setRawPredictionCol
Sets the value of rawPredictionCol.
rawPredictionCol
setThresholds(value)
setThresholds
Sets the value of thresholds.
thresholds
transform(dataset[, params])
transform
Transforms the input dataset with optional parameters.
write()
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.
impurity
labelCol
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.
numNodes
Return number of nodes of the decision tree.
params
Returns all params ordered by name.
seed
supportedImpurities
toDebugString
Full description of model.
weightCol
Methods Documentation
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.
Extra parameters to copy to the new instance
JavaParams
Copy of this instance
extra param values
merged param map
New in version 1.6.0.
Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.
New in version 3.0.0.
New in version 1.3.0.
pyspark.sql.DataFrame
input dataset
an optional param map that overrides embedded params.
transformed dataset
Attributes Documentation
New in version 1.5.0.
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.
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.
New in version 2.0.0.
Notes
Feature importance for single decision trees can have high variance due to correlated predictor variables. Consider using a RandomForestClassifier to determine feature importance instead.
RandomForestClassifier
New in version 2.1.0.
Returns the number of features the model was trained on. If unknown, returns -1
Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.
dir()
Param