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LightGBM

website/versioned_docs/version-1.0.4/Quick Examples/estimators/_LightGBM.md

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import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; import DocTable from "@theme/DocumentationTable";

LightGBMClassifier

<Tabs defaultValue="py" values={[ {label: Python, value: py}, {label: Scala, value: scala}, ]}> <TabItem value="py">

<!--pytest-codeblocks:cont-->
python
from synapse.ml.lightgbm import *

lgbmClassifier = (LightGBMClassifier()
      .setFeaturesCol("features")
      .setRawPredictionCol("rawPrediction")
      .setDefaultListenPort(12402)
      .setNumLeaves(5)
      .setNumIterations(10)
      .setObjective("binary")
      .setLabelCol("labels")
      .setLeafPredictionCol("leafPrediction")
      .setFeaturesShapCol("featuresShap"))
</TabItem> <TabItem value="scala">
scala
import com.microsoft.azure.synapse.ml.lightgbm._

val lgbmClassifier = (new LightGBMClassifier()
      .setFeaturesCol("features")
      .setRawPredictionCol("rawPrediction")
      .setDefaultListenPort(12402)
      .setNumLeaves(5)
      .setNumIterations(10)
      .setObjective("binary")
      .setLabelCol("labels")
      .setLeafPredictionCol("leafPrediction")
      .setFeaturesShapCol("featuresShap"))
</TabItem> </Tabs>

<DocTable className="LightGBMClassifier" py="synapse.ml.lightgbm.html#module-synapse.ml.lightgbm.LightGBMClassifier" scala="com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassifier.html" csharp="classSynapse_1_1ML_1_1Lightgbm_1_1LightGBMClassifier.html" sourceLink="https://github.com/microsoft/SynapseML/blob/master/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMClassifier.scala" />

LightGBMRanker

<Tabs defaultValue="py" values={[ {label: Python, value: py}, {label: Scala, value: scala}, ]}> <TabItem value="py">

<!--pytest-codeblocks:cont-->
python
from synapse.ml.lightgbm import *

lgbmRanker = (LightGBMRanker()
      .setLabelCol("labels")
      .setFeaturesCol("features")
      .setGroupCol("query")
      .setDefaultListenPort(12402)
      .setRepartitionByGroupingColumn(False)
      .setNumLeaves(5)
      .setNumIterations(10))
</TabItem> <TabItem value="scala">
scala
import com.microsoft.azure.synapse.ml.lightgbm._

val lgbmRanker = (new LightGBMRanker()
      .setLabelCol("labels")
      .setFeaturesCol("features")
      .setGroupCol("query")
      .setDefaultListenPort(12402)
      .setRepartitionByGroupingColumn(false)
      .setNumLeaves(5)
      .setNumIterations(10))
</TabItem> </Tabs>

<DocTable className="LightGBMRanker" py="synapse.ml.lightgbm.html#module-synapse.ml.lightgbm.LightGBMRanker" scala="com/microsoft/azure/synapse/ml/lightgbm/LightGBMRanker.html" csharp="classSynapse_1_1ML_1_1Lightgbm_1_1LightGBMRanker.html" sourceLink="https://github.com/microsoft/SynapseML/blob/master/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRanker.scala" />

LightGBMRegressor

<Tabs defaultValue="py" values={[ {label: Python, value: py}, {label: Scala, value: scala}, ]}> <TabItem value="py">

<!--pytest-codeblocks:cont-->
python
from synapse.ml.lightgbm import *

lgbmRegressor = (LightGBMRegressor()
      .setLabelCol("labels")
      .setFeaturesCol("features")
      .setDefaultListenPort(12402)
      .setNumLeaves(5)
      .setNumIterations(10))
</TabItem> <TabItem value="scala">
scala
import com.microsoft.azure.synapse.ml.lightgbm._

val lgbmRegressor = (new LightGBMRegressor()
      .setLabelCol("labels")
      .setFeaturesCol("features")
      .setDefaultListenPort(12402)
      .setNumLeaves(5)
      .setNumIterations(10))
</TabItem> </Tabs>

<DocTable className="LightGBMRegressor" py="synapse.ml.lightgbm.html#module-synapse.ml.lightgbm.LightGBMRegressor" scala="com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressor.html" csharp="classSynapse_1_1ML_1_1Lightgbm_1_1LightGBMRegressor.html" sourceLink="https://github.com/microsoft/SynapseML/blob/master/lightgbm/src/main/scala/com/microsoft/azure/synapse/ml/lightgbm/LightGBMRegressor.scala" />