some practical hints

Author: ogrisel

tutorial/which_algorithm.rst

@@ -1,8 +1,22 @@
 How to select the right algorithm for the task
 ==============================================
 
+To conclude this session here are some practical hints for selecting
+the right algorithm when facing a practical problem.
 
-Some practical hints for selecting the right algorithm when facing
-a practical problem.
+- If the data is high dimensional and sparse (text data), most of the time
+  linear classifiers with a bit of regularization will work well.
 
+- If the data is dense, low to medium dimensional: try to further reduce the
+  dimensionality with PCA for instance and try both linear and non linear
+  models (e.g. SVC with RBF kernel).
+
+- ``SVC`` with gaussian RBF kernel and ``KMeans`` clustering can
+  benefit a lot from data normalization with (``PCA`` or ``RandomizedPCA``
+  with ``whiten=True``). Try various values for ``n_components`` with grid
+  search to be sure no to truncate the data too hard.
+
+- There is no free lunch: the best algorithm is data dependant. If
+  you try many different models, reserve a held out evaluation set
+  that is not used during the model selection process.