PySpark API Supportability Matrix | LogisticRegressionModel | pyspark2teradataml - LogisticRegressionModel - 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 LogisticRegression through teradataml Open source ML functions.

Attributes/Methods

Attribute/Method Name Supported Notes
clear  
copy  
evaluate  
explainParam  
explainParams  
extractParamMap  
getAggregationDepth  
getElasticNetParam  
getFamily  
getFeaturesCol  
getFitIntercept  
getLabelCol  
getLowerBoundsOnCoefficients  
getLowerBoundsOnIntercepts  
getMaxBlockSizeInMB  
getMaxIter  
getOrDefault  
getParam  
getPredictionCol  
get ProbabilityCol  
getRawPredictionCol  
getRegParam  
getStandardization  
getThreshold  
getThresholds  
getTol  
getUpperBoundsOnCoefficients  
getUpperBoundsOnIntercepts  
getWeightCol  
hasDefault  
hasParam  
isDefined  
isSet  
load  
predict  
predictProbability  
predictRaw  
read  
save  
set  
setFeaturesCol  
setPredictionCol  
setProbabilityCol  
setRawPredictionCol  
setThreshold  
setThresholds  
transform  
write  
aggregationDepth  
coefficientMatrix  
coefficients  
elasticNetParam  
family  
featuresCol  
fitIntercept  
hasSummary  
intercept  
interceptVector  
labelCol  
lowerBoundsOnCoefficients  
lowerBoundsOnIntercepts  
maxBlockSizeInMB  
maxIter  
numClasses  
numFeatures  
params  
predictionCol  
probabilityCol  
rawPredictionCol  
regParam  
standardization  
summary  
threshold  
thresholds  
tol  
upperBoundsOnCoefficients  
upperBoundsOnIntercepts  
weightCol