DOC clarify feature importance calculation
@@ -260,11 +260,16 @@ respect to the predictability of the target variable. Features used at
the top of the tree contribute to the final prediction decision of a
larger fraction of the input samples. The **expected fraction of the
samples** they contribute to can thus be used as an estimate of the
-**relative importance of the features**.
+**relative importance of the features**. In scikit-learn, the fraction of
+samples a feature contributes to is combined with the decrease in impurity
+from splitting them to create a normalized estimate of the predictive power
+of that feature.
-By **averaging** those expected activity rates over several randomized
+By **averaging** the estimates of predictive ability over several randomized
trees one can **reduce the variance** of such an estimate and use it
-for feature selection.
+for feature selection. This is known as the mean decrease in impurity, or MDI.
+Refer to [L2014]_ for more information on MDI and feature importance
+evaluation with Random Forests.
The following example shows a color-coded representation of the relative
importances of each individual pixel for a face recognition task using
@@ -288,6 +293,12 @@ to the prediction function.
.. _random_trees_embedding:
+.. topic:: References
+
+ .. [L2014] G. Louppe,
+ "Understanding Random Forests: From Theory to Practice",
+ PhD Thesis, U. of Liege, 2014.
+
Totally Random Trees Embedding
------------------------------
