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diff --git a/docs/mllib-ensembles.md b/docs/mllib-ensembles.md index 7521fb14a7..1e00b2083e 100644 --- a/docs/mllib-ensembles.md +++ b/docs/mllib-ensembles.md @@ -9,7 +9,7 @@ displayTitle: <a href="mllib-guide.html">MLlib</a> - Ensembles An [ensemble method](http://en.wikipedia.org/wiki/Ensemble_learning) is a learning algorithm which creates a model composed of a set of other base models. -MLlib supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBosotedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest). +MLlib supports two major ensemble algorithms: [`GradientBoostedTrees`](api/scala/index.html#org.apache.spark.mllib.tree.GradientBoostedTrees) and [`RandomForest`](api/scala/index.html#org.apache.spark.mllib.tree.RandomForest). Both use [decision trees](mllib-decision-tree.html) as their base models. ## Gradient-Boosted Trees vs. Random Forests |