Arguments
This function internally uses scikit-learn function GradientBoostingClassifier through teradataml Open source ML functions.
Transformed data won’t have features, rawPrediction, probability columns.
| PySpark Argument Name | Open Source Function Argument Name | Notes |
|---|---|---|
| predictionCol | Not yet available. | |
| maxDepth | max_depth | |
| maxBins | Not yet available. | |
| minInstancesPerNode | min_samples_split | PySpark takes int value (>= 1) default value is 1, teradatamlspk supports int value (>1) or float in (0.0, 1.0]. Default value is 1.0. |
| minInfoGain | Not yet available. | |
| maxMemoryInMB | Not yet available. | |
| cacheNodeIds | Not yet available. | |
| checkpointInterval | Not yet available. | |
| lossType | loss | Supported values ['log_loss', 'exponential'] |
| maxIter | Not yet available. | |
| stepSize | learning_rate | |
| seed | random_state | |
| subsamplingRate | subsample | |
| impurity | criterion | Supported values ['friedman_mse','mse','mae'] |
| featureSubsetStrategy | Not yet available. | |
| validationTol | Not yet available. | |
| validationIndicatorCol | Not yet available. | |
| leafCol | Not yet available. | |
| minWeightFractionPerNode | min_weight_fraction_leaf | |
| weightCol | Not yet available. |
Attributes/Methods
| Attribute/Method Name | Supported | Notes |
|---|---|---|
| clear | ||
| copy | ||
| explainParam | ||
| explainParams | ||
| extractParamMap | ||
| fit | ||
| fitMultiple | ||
| getCacheNodeIds | ||
| getCheckpointInterval | ||
| getFeatureSubsetStrategy | ||
| getFeaturesCol | ||
| getImpurity | ||
| getLabelCol | ||
| getLeafCol | ||
| getLossType | ||
| getMaxBins | ||
| getMaxDepth | ||
| getMaxIter | ||
| getMaxMemoryInMB | ||
| getMinInfoGain | ||
| getMinInstancesPerNode | ||
| getMinWeightFractionPerNode | ||
| getOrDefault | ||
| getParam | ||
| getPredictionCol | ||
| getProbabilityCol | ||
| getRawPredictionCol | ||
| getSeed | ||
| getStepSize | ||
| getSubsamplingRate | ||
| getThresholds | ||
| getValidationIndicatorCol | ||
| getValidationTol | ||
| getWeightCol | ||
| hasDefault | ||
| hasParam | ||
| isDefined | ||
| isSet | ||
| load | ||
| read | ||
| save | ||
| set | ||
| setCacheNodeIds | ||
| setCheckpointInterval | ||
| setFeatureSubsetStrategy | ||
| setFeaturesCol | ||
| setImpurity | ||
| setLabelCol | ||
| setLeafCol | ||
| setLossType | ||
| setMaxBins | ||
| setMaxDepth | ||
| setMaxIter | ||
| setMaxMemoryInMB | ||
| setMinInfoGain | ||
| setMinInstancesPerNode | ||
| setMinWeightFractionPerNode | ||
| setParams | ||
| setPredictionCol | ||
| setProbabilityCol | ||
| setRawPredictionCol | ||
| setSeed | ||
| setStepSize | ||
| setSubSamplingRate | ||
| setThresholds | ||
| setValidationIndicatorCol | ||
| setWeightCol | ||
| write | ||
| cacheNodeIds | ||
| checkpointInterval | ||
| featureSubsetStrategy | ||
| featuresCol | ||
| impurity | ||
| labelCol | ||
| leafCol | ||
| lossType | ||
| maxBins | ||
| maxDepth | ||
| maxIter | ||
| maxMemoryInMB | ||
| minInfoGain | ||
| minInstancesPerNode | ||
| minWeightFractionPerNode | ||
| params | ||
| predictionCol | ||
| probabilityCol | ||
| rawPredictionCol | ||
| seed | ||
| stepSize | ||
| subsamplingRate | ||
| supportedFeatureSubsetStrategies | ||
| supportedImpurities | ||
| supportedLossTypes | ||
| thresholds | ||
| validationIndicatorCol | ||
| validationTol | ||
| weightCol |