Add warning messages when full data-set is used

[ArturoAmorQ]

The full data-set (no train-test split or cv) is used for modeling in the following notebooks:

• linear_regression_without_sklearn.py
• linear_models_ex_01.py and its solution
• linear_regression_in_sklearn.py
• linear_models_ex_02.py and its solution
• linear_regression_non_linear_link.py
• linear_models_ex_04.py and its solution
• logistic_regression_non_linear.py
• trees_regression.py
• trees_ex_02.py and its solution
• ensemble_bagging.py
• ensemble_adaboost.py

This has been a source of confusion (see for instance this forum question).

We should add a Warning message similar (but adapted to each case) to the one in logistic_regression_non_linear.py:

Warning: Be aware that we fit and will check the boundary decision of the classifier on the same dataset without splitting the dataset into a training set and a testing set. While this is a bad practice, we use it for the sake of simplicity to depict the model behavior. Always use cross-validation when you want to assess the generalization performance of a machine-learning model.

Additionally, a Warning message should be added in the following notebooks

• linear_models_ex_01.py and its solution
• linear_regression_in_sklearn.py
• linear_models_ex_02.py and its solution
• linear_regression_non_linear_link.py

where we remind the user that scoring the model in the full data-set is not necessarily wrong but provides no info about under/over-fitting.

What do you think?