DOC: datacraft feedbacks (#725)

Author: FaustinPulveric

doc/images/cp_prefit.png

@@ -26,15 +26,15 @@ Split conformal predictions with a pre-trained model
 ------------------------------------------------------------------------------------
 
 .. image:: images/cp_prefit.png
-    :width: 600
+    :width: 800
     :align: center
 
 
 Split conformal predictions with an untrained model
 ------------------------------------------------------------------------------------
 
 .. image:: images/cp_split.png
-    :width: 600
+    :width: 800
     :align: center
 
 

doc/images/cp_split.png

@@ -259,6 +259,11 @@ heteroscedastic data. It uses quantile regressors with different quantile values
 the prediction bounds. The residuals of these methods are used to create the guaranteed
 coverage value.
 
+The figure below illustrates the conformalized quantile regression method.
+
+.. image:: images/cqr.png
+   :width: 800
+
 Notations and Definitions
 -------------------------
 - :math:`\mathcal{I}_1` is the set of indices of the data in the training set.