LightGBMRegressor

class LightGBMRegressor.LightGBMRegressionModel(java_model=None)

Bases: mmlspark._LightGBMRegressor._LightGBMRegressionModel

getFeatureImportances(importance_type='split')

Get the feature importances as a list. The importance_type can be “split” or “gain”.

static loadNativeModelFromFile(filename, labelColName='label', featuresColName='features', predictionColName='prediction')

Load the model from a native LightGBM text file.

static loadNativeModelFromString(model, labelColName='label', featuresColName='features', predictionColName='prediction')

Load the model from a native LightGBM model string.

saveNativeModel(filename, overwrite=True)

Save the booster as string format to a local or WASB remote location.

class LightGBMRegressor.LightGBMRegressor(alpha=0.9, baggingFraction=1.0, baggingFreq=0, baggingSeed=3, boostFromAverage=True, boostingType='gbdt', categoricalSlotIndexes=None, categoricalSlotNames=None, defaultListenPort=12400, earlyStoppingRound=0, featureFraction=1.0, featuresCol='features', labelCol='label', lambdaL1=0.0, lambdaL2=0.0, learningRate=0.1, maxBin=255, maxDepth=-1, minSumHessianInLeaf=0.001, modelString='', numIterations=100, numLeaves=31, objective='regression', parallelism='data_parallel', predictionCol='prediction', timeout=1200.0, tweedieVariancePower=1.5, validationIndicatorCol=None, verbosity=1, weightCol=None)

Bases: mmlspark._LightGBMRegressor._LightGBMRegressor