Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/3461#discussion_r21111725
--- Diff: docs/mllib-decision-tree.md ---
@@ -103,36 +106,73 @@ and the resulting `$M-1$` split candidates are
considered.
### Stopping rule
-The recursive tree construction is stopped at a node when one of the two
conditions is met:
+The recursive tree construction is stopped at a node when one of the
following conditions is met:
1. The node depth is equal to the `maxDepth` training parameter.
-2. No split candidate leads to an information gain at the node.
+2. No split candidate leads to an information gain greater than
`minInfoGain`.
+3. No split candidate produces child nodes which each have at least
`minInstancesPerNode` training instances.
+
+## Usage tips
+
+We include a few guidelines for using decision trees by discussing the
various parameters.
+There are many parameters, put in order here with the most imporant first.
New users should mainly consider the "Problem specification parameters"
section below and the `maxDepth` parameter.
+
+### Problem specification parameters
+
+These parameters describe the problem you want to solve and your dataset.
+They should be specified and do not require tuning.
+
+* **`algo`**: `Classification` or `Regression`
+
+* **`numClasses`**: Number of classes (for `Classification` only)
+
+* **`categoricalFeaturesInfo`**: Specifies which features are categorical
and how many categorical values each of those features can take. This is given
as a map from feature indices to feature arity (number of categories). Any
features not in this map are treated as continuous.
+ * E.g., `Map(1 -> 2, 4 -> 10)` specifies that feature `1` is binary
(taking values `0` or `1`) and that feature `4` has 10 categories (values `{0,
1, ..., 9}`). Note that feature indices are 0-based: features `1` and `4` are
the 2nd and 5th elements of an instance's feature vector.
--- End diff --
good idea
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