This function internally uses scikit-learn function DecisionTreeClassifier through teradataml Open source ML functions.
Attribute/Method Name | Supported | Notes |
---|---|---|
clear | ||
copy | ||
explainParam | ||
explainParams | ||
extractParamMap | ||
getCacheNodeIds | ||
getCheckpointInterval | ||
getFeaturesCol | ||
getImpurity | ||
getLabelCol | ||
getLeafCol | ||
getMaxBins | ||
getMaxDepth | ||
getMaxMemoryInMB | ||
getMinInfoGain | ||
getMinInstancesPerNode | ||
getOrDefault | ||
getParam | ||
getPredictionCol | ||
getProbabilityCol | ||
getRawPredictionCol | ||
getSeed | ||
getThresholds | ||
getWeightCol | ||
hasDefault | ||
hasParam | ||
isDefined | ||
isSet | ||
load | ||
predict | ||
predictLeaf | ||
predictProbability | ||
predictRaw | ||
read | ||
save | ||
set | ||
setFeaturesCol | ||
setLeafCol | ||
setPredictionCol | ||
setProbabilityCol | ||
setRawPredictionCol | ||
setThresholds | ||
transform | ||
write | ||
cacheNodeIds | ||
checkpointInterval | ||
depth | ||
featureImportances | PySpark returns a DenseVector, whereas teradatamlspk returns a numpy array. | |
featuresCol | ||
impurity | ||
labelCol | ||
leafCol | ||
maxBins | ||
maxDepth | ||
maxMemoryInMB | ||
minInfoGain | ||
minInstancesPerNode | ||
numClasses | ||
numFeatures | ||
numNodes | ||
params | ||
predictionCol | ||
probabilityCol | ||
rawPredictionCol | ||
seed | ||
supportedImpurities | ||
thresholds | ||
toDebugString | ||
weightCol |