.. _example_plot_train_error_vs_test_error.py:


=========================
Train error vs Test error
=========================

Illustration of how the performance of an estimator on unseen data (test data)
is not the same as the performance on training data. As the regularization
increases the performance on train decreases while the performance on test
is optimal within a range of values of the regularization parameter.
The example with an Elastic-Net regression model and the performance is
measured using the explained variance a.k.a. R^2.




.. image:: images/plot_train_error_vs_test_error_001.png
    :align: center


**Script output**::

  Optimal regularization parameter : 0.000335292414925



**Python source code:** :download:`plot_train_error_vs_test_error.py <plot_train_error_vs_test_error.py>`

.. literalinclude:: plot_train_error_vs_test_error.py
    :lines: 14-

**Total running time of the example:**  3.03 seconds
( 0 minutes  3.03 seconds)