PySpark API Supportability Matrix | RandomForestClassificationModel | pyspark2teradataml - RandomForestClassificationModel - Teradata Package for Python

Teradata® pyspark2teradataml User Guide

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for Python
Release Number
20.00
Published
December 2024
ft:locale
en-US
ft:lastEdition
2024-12-18
dita:mapPath
oeg1710443196055.ditamap
dita:ditavalPath
ayr1485454803741.ditaval
dita:id
oeg1710443196055
Product Category
Teradata Vantage

This function internally uses scikit-learn function randomforestclassifier through teradataml Open source ML functions.

Attributes/Methods

Attribute/Method Name Supported Notes
clear  
copy  
evaluate  
explainParam  
explainParams  
extractParamMap  
getBootstrap  
getCacheNodeIds  
getCheckpointInterval  
getFeatureSubsetStrategy  
getFeaturesCol  
getImpurity  
getLabelCol  
getLeafCol  
getMaxBins  
getMaxDepth  
getMaxMemoryInMB  
getMinInfoGain  
getMinInstancesPerNode  
getMinWeightFractionPerNode  
getOrDefault  
getParam  
getPredictionCol  
getProbabilityCol  
getRawPredictionCol  
getSeed  
getStepSize  
getSubsamplingRate  
getThresholds  
getWeightCol  
hasDefault  
hasParam  
isDefined  
isSet  
load  
predict  
predictLeaf  
predictProbability  
predictRaw  
read  
save  
set  
setFeaturesCol  
setLeafCol  
setPredictionCol  
setProbabilityCol  
setRawPredictionCol  
setThresholds  
transform  
write  
bootstrap  
cacheNodeIds  
checkpointInterval  
featureSubsetStrategy  
featuresCol  
impurity  
labelCol  
leafCol  
maxBins  
maxDepth  
maxMemoryInMB  
minInfoGain  
minInstancesPerNode  
minWeightFractionPerNode  
numClasses  
numFeatures  
numTrees  
params  
predictionCol  
probabilityCol  
rawPredictionCol  
seed  
subsamplingRate  
summary  
supportedFeatureSubsetStrategies  
supportedImpurities  
thresholds  
toDebugString  
totalNumNodes  
treeWeights  
trees  
weightCol