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 |