add example recursive feature addition (#197)

Author: solegalli

docs/selection/RecursiveFeatureAddition

@@ -0,0 +1,81 @@
+RecursiveFeatureAddition
+========================
+
+API Reference
+-------------
+
+.. autoclass:: feature_engine.selection.RecursiveFeatureAddition
+    :members:
+
+
+Example
+-------
+
+.. code:: python
+
+    import pandas as pd
+    from sklearn.datasets import load_diabetes
+    from sklearn.linear_model import LinearRegression
+    from feature_engine.selection import RecursiveFeatureElimination
+
+    # load dataset
+    diabetes_X, diabetes_y = load_diabetes(return_X_y=True)
+    X = pd.DataFrame(diabetes_X)
+    y = pd.DataFrame(diabetes_y)
+
+    # initialize linear regresion estimator
+    linear_model = LinearRegression()
+
+    # initialize feature selector
+    tr = RecursiveFeatureElimination(estimator=linear_model, scoring="r2", cv=3)
+
+    # fit transformer
+    Xt = tr.fit_transform(X, y)
+
+    # get the initial linear model performance, using all features
+    tr.initial_model_performance_
+
+.. code:: python
+
+    0.488702767247119
+
+..  code:: python
+
+    # Get the performance drift of each feature
+    tr.performance_drifts_
+
+..  code:: python
+
+    {4: 0,
+     8: 0.2837159006046677,
+     2: 0.1377700238871593,
+     5: 0.0023329006089969906,
+     3: 0.0187608758643259,
+     1: 0.0027994385024313617,
+     7: 0.0026951300105543807,
+     6: 0.002683967832484757,
+     9: 0.0003040126429713075,
+     0: -0.007386876030245182}
+
+..  code:: python
+
+    # get the selected features
+    tr.selected_features_
+
+..  code:: python
+
+    [4, 8, 2, 3]
+
+..  code:: python
+
+    print(Xt.head())
+
+..  code:: python
+
+              4         8         2         3
+    0 -0.044223  0.019908  0.061696  0.021872
+    1 -0.008449 -0.068330 -0.051474 -0.026328
+    2 -0.045599  0.002864  0.044451 -0.005671
+    3  0.012191  0.022692 -0.011595 -0.036656
+    4  0.003935 -0.031991 -0.036385  0.021872
+