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Diffstat (limited to 'docs/ml-classification-regression.md')
| -rw-r--r-- | docs/ml-classification-regression.md | 4 |
1 files changed, 2 insertions, 2 deletions
diff --git a/docs/ml-classification-regression.md b/docs/ml-classification-regression.md index 88457d4bb1..d7e5521cbc 100644 --- a/docs/ml-classification-regression.md +++ b/docs/ml-classification-regression.md @@ -815,7 +815,7 @@ The main differences between this API and the [original MLlib ensembles API](mll ## Random Forests [Random forests](http://en.wikipedia.org/wiki/Random_forest) -are ensembles of [decision trees](ml-decision-tree.html). +are ensembles of [decision trees](ml-classification-regression.html#decision-trees). Random forests combine many decision trees in order to reduce the risk of overfitting. The `spark.ml` implementation supports random forests for binary and multiclass classification and for regression, using both continuous and categorical features. @@ -896,7 +896,7 @@ All output columns are optional; to exclude an output column, set its correspond ## Gradient-Boosted Trees (GBTs) [Gradient-Boosted Trees (GBTs)](http://en.wikipedia.org/wiki/Gradient_boosting) -are ensembles of [decision trees](ml-decision-tree.html). +are ensembles of [decision trees](ml-classification-regression.html#decision-trees). GBTs iteratively train decision trees in order to minimize a loss function. The `spark.ml` implementation supports GBTs for binary classification and for regression, using both continuous and categorical features. |