.. _example_gaussian_process_plot_gpc.py:


====================================================================
Probabilistic predictions with Gaussian process classification (GPC)
====================================================================

This example illustrates the predicted probability of GPC for an RBF kernel
with different choices of the hyperparameters. The first figure shows the
predicted probability of GPC with arbitrarily chosen hyperparameters and with
the hyperparameters corresponding to the maximum log-marginal-likelihood (LML).

While the hyperparameters chosen by optimizing LML have a considerable larger
LML, they perform slightly worse according to the log-loss on test data. The
figure shows that this is because they exhibit a steep change of the class
probabilities at the class boundaries (which is good) but have predicted
probabilities close to 0.5 far away from the class boundaries (which is bad)
This undesirable effect is caused by the Laplace approximation used
internally by GPC.

The second figure shows the log-marginal-likelihood for different choices of
the kernel's hyperparameters, highlighting the two choices of the
hyperparameters used in the first figure by black dots.



.. rst-class:: horizontal


    *

      .. image:: images/plot_gpc_000.png
            :scale: 47

    *

      .. image:: images/plot_gpc_001.png
            :scale: 47


**Script output**::

  Log Marginal Likelihood (initial): -17.598
  Log Marginal Likelihood (optimized): -3.875
  Accuracy: 1.000 (initial) 1.000 (optimized)
  Log-loss: 0.214 (initial) 0.319 (optimized)



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

.. literalinclude:: plot_gpc.py
    :lines: 23-

**Total running time of the example:**  5.57 seconds
( 0 minutes  5.57 seconds)