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Diffstat (limited to 'docs/mllib-ensembles.md')
| -rw-r--r-- | docs/mllib-ensembles.md | 44 |
1 files changed, 32 insertions, 12 deletions
diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md index 1e00b2083e..fc587298f7 100644 --- a/docs/mllib-ensembles.md +++ b/docs/mllib-ensembles.md @@ -95,7 +95,9 @@ The test error is calculated to measure the algorithm accuracy. <div class="codetabs"> -<div data-lang="scala"> +<div data-lang="scala" markdown="1"> +Refer to the [`RandomForest` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest) and [`RandomForestModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.model.RandomForestModel) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.tree.RandomForest import org.apache.spark.mllib.tree.model.RandomForestModel @@ -135,7 +137,9 @@ val sameModel = RandomForestModel.load(sc, "myModelPath") {% endhighlight %} </div> -<div data-lang="java"> +<div data-lang="java" markdown="1"> +Refer to the [`RandomForest` Java docs](api/java/org/apache/spark/mllib/tree/RandomForest.html) and [`RandomForestModel` Java docs](api/java/org/apache/spark/mllib/tree/model/RandomForestModel.html) for details on the API. + {% highlight java %} import scala.Tuple2; import java.util.HashMap; @@ -200,7 +204,8 @@ RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath"); {% endhighlight %} </div> -<div data-lang="python"> +<div data-lang="python" markdown="1"> +Refer to the [`RandomForest` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.RandomForest) and [`RandomForest` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.RandomForestModel) for more details on the API. {% highlight python %} from pyspark.mllib.tree import RandomForest, RandomForestModel @@ -246,7 +251,9 @@ The Mean Squared Error (MSE) is computed at the end to evaluate <div class="codetabs"> -<div data-lang="scala"> +<div data-lang="scala" markdown="1"> +Refer to the [`RandomForest` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest) and [`RandomForestModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.model.RandomForestModel) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.tree.RandomForest import org.apache.spark.mllib.tree.model.RandomForestModel @@ -286,7 +293,9 @@ val sameModel = RandomForestModel.load(sc, "myModelPath") {% endhighlight %} </div> -<div data-lang="java"> +<div data-lang="java" markdown="1"> +Refer to the [`RandomForest` Java docs](api/java/org/apache/spark/mllib/tree/RandomForest.html) and [`RandomForestModel` Java docs](api/java/org/apache/spark/mllib/tree/model/RandomForestModel.html) for details on the API. + {% highlight java %} import java.util.HashMap; import scala.Tuple2; @@ -354,7 +363,8 @@ RandomForestModel sameModel = RandomForestModel.load(sc.sc(), "myModelPath"); {% endhighlight %} </div> -<div data-lang="python"> +<div data-lang="python" markdown="1"> +Refer to the [`RandomForest` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.RandomForest) and [`RandomForest` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.RandomForestModel) for more details on the API. {% highlight python %} from pyspark.mllib.tree import RandomForest, RandomForestModel @@ -479,7 +489,9 @@ The test error is calculated to measure the algorithm accuracy. <div class="codetabs"> -<div data-lang="scala"> +<div data-lang="scala" markdown="1"> +Refer to the [`GradientBoostedTrees` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees) and [`GradientBoostedTreesModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.model.GradientBoostedTreesModel) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.tree.GradientBoostedTrees import org.apache.spark.mllib.tree.configuration.BoostingStrategy @@ -518,7 +530,9 @@ val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath") {% endhighlight %} </div> -<div data-lang="java"> +<div data-lang="java" markdown="1"> +Refer to the [`GradientBoostedTrees` Java docs](api/java/org/apache/spark/mllib/tree/GradientBoostedTrees.html) and [`GradientBoostedTreesModel` Java docs](api/java/org/apache/spark/mllib/tree/model/GradientBoostedTreesModel.html) for details on the API. + {% highlight java %} import scala.Tuple2; import java.util.HashMap; @@ -583,7 +597,8 @@ GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "m {% endhighlight %} </div> -<div data-lang="python"> +<div data-lang="python" markdown="1"> +Refer to the [`GradientBoostedTrees` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.GradientBoostedTrees) and [`GradientBoostedTreesModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.GradientBoostedTreesModel) for more details on the API. {% highlight python %} from pyspark.mllib.tree import GradientBoostedTrees, GradientBoostedTreesModel @@ -627,7 +642,9 @@ The Mean Squared Error (MSE) is computed at the end to evaluate <div class="codetabs"> -<div data-lang="scala"> +<div data-lang="scala" markdown="1"> +Refer to the [`GradientBoostedTrees` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees) and [`GradientBoostedTreesModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.tree.model.GradientBoostedTreesModel) for details on the API. + {% highlight scala %} import org.apache.spark.mllib.tree.GradientBoostedTrees import org.apache.spark.mllib.tree.configuration.BoostingStrategy @@ -665,7 +682,9 @@ val sameModel = GradientBoostedTreesModel.load(sc, "myModelPath") {% endhighlight %} </div> -<div data-lang="java"> +<div data-lang="java" markdown="1"> +Refer to the [`GradientBoostedTrees` Java docs](api/java/org/apache/spark/mllib/tree/GradientBoostedTrees.html) and [`GradientBoostedTreesModel` Java docs](api/java/org/apache/spark/mllib/tree/model/GradientBoostedTreesModel.html) for details on the API. + {% highlight java %} import scala.Tuple2; import java.util.HashMap; @@ -736,7 +755,8 @@ GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(sc.sc(), "m {% endhighlight %} </div> -<div data-lang="python"> +<div data-lang="python" markdown="1"> +Refer to the [`GradientBoostedTrees` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.GradientBoostedTrees) and [`GradientBoostedTreesModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.tree.GradientBoostedTreesModel) for more details on the API. {% highlight python %} from pyspark.mllib.tree import GradientBoostedTrees, GradientBoostedTreesModel |