This function internally uses scikit-learn function DecisionTreeRegressor through teradataml Open source ML functions.
Attributes/Methods
| Attribute/Method Name | Supported | Notes |
|---|---|---|
| clear | ||
| copy | ||
| explainParam | ||
| explainParams | ||
| extractParamMap | ||
| getCacheNodeIds | ||
| getCheckpointInterval | ||
| getFeaturesCol | ||
| getImpurity | ||
| getLabelCol | ||
| getLeafCol | ||
| getMaxBins | ||
| getMaxDepth | ||
| getMaxMemoryInMB | ||
| getMinInfoGain | ||
| getMinInstancesPerNode | ||
| getMinWeightFractionPerNode | ||
| getOrDefault | ||
| getParam | ||
| getPredictionCol | ||
| getSeed | ||
| getVarianceCol | ||
| getWeightCol | ||
| hasDefault | ||
| hasParam | ||
| isDefined | ||
| isSet | ||
| load | ||
| predict | ||
| predictLeaf | ||
| read | ||
| save | ||
| set | ||
| setFeaturesCol | ||
| setLeafCol | ||
| setPredictionCol | ||
| setVarianceCol | ||
| 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 | ||
| minWeightFractionPerNode | ||
| numNodes | ||
| params | ||
| predictionCol | ||
| seed | ||
| supportedImpurities | ||
| toDebugString | ||
| varianceCol | ||
| weightCol |