This function internally uses scikit-learn function LinearRegression through teradataml Open source ML functions.
Attributes/Methods
Attribute/Method Name | Supported | Notes |
---|---|---|
copy | ||
evaluate | ||
explainParam | ||
explainParams | ||
extractParamMap | ||
getAggregationDepth | ||
getElasticNetParam | ||
getEpsilon | ||
getFeaturesCol | ||
getFitIntercept | ||
getLabelCol | ||
getLoss | ||
getMaxBlockSizeInMB | ||
getMaxIter | ||
getOrDefault | ||
getParam | ||
getPredictionCol | ||
getRegParam | ||
getSolver | ||
getStandardization | ||
getThreshold | ||
getTol | ||
getWeightCol | ||
hasDefault | ||
hasParam | ||
isDefined | ||
isSet | ||
load | ||
predict | ||
read | ||
save | ||
set | ||
setFeaturesCol | ||
setPredictionCol | ||
transform | ||
write | ||
aggregationDepth | ||
coefficients | PySpark returns a DenseVector, whereas teradatamlspk returns a numpy array. | |
elasticNetParam | ||
epsilon | ||
featuresCol | ||
fitIntercept | ||
hasSummary | ||
intercept | ||
labelCol | ||
loss | ||
maxBlockSizeInMB | ||
maxIter | ||
params | ||
predictionCol | ||
regParam | ||
scale | ||
solver | ||
standardization | ||
summary | ||
tol | ||
weightCol |