updated forest vs boosting comparison

Author: jkbradley

docs/mllib-ensembles.md

@@ -16,10 +16,10 @@ Both use [decision trees](mllib-decision-tree.html) as their base models.
 
 Both [Gradient-Boosted Trees (GBTs)](mllib-ensembles.html#Gradient-Boosted-Trees-(GBTS)) and [Random Forests](mllib-ensembles.html#Random-Forests) are algorithms for learning ensembles of trees, but the training processes are different.  There are several practical trade-offs:
 
- * GBTs may be able to achieve the same accuracy using fewer trees, so the model produced may be smaller (faster for test time prediction).
  * GBTs train one tree at a time, so they can take longer to train than random forests.  Random Forests can train multiple trees in parallel.
-   * On the other hand, it is often reasonable to use smaller trees with GBTs than with Random Forests, and training smaller trees takes less time.
- * Random Forests can be less prone to overfitting.  Training more trees in a Random Forest reduces the likelihood of overfitting, but training more trees with GBTs increases the likelihood of overfitting.
+   * On the other hand, it is often reasonable to use smaller (shallower) trees with GBTs than with Random Forests, and training smaller trees takes less time.
+ * Random Forests can be less prone to overfitting.  Training more trees in a Random Forest reduces the likelihood of overfitting, but training more trees with GBTs increases the likelihood of overfitting.  (In statistical language, Random Forests reduce variance by using more trees, whereas GBTs reduce bias by using more trees.)
+ * Random Forests can be easier to tune since performance improves monotonically with the number of trees (whereas performance can start to decrease for GBTs if the number of trees grows too large).
 
 In short, both algorithms can be effective, and the choice should be based on the particular dataset.