Predict Trip duration
=====================

In this section, I will validate decision tree the model to predict the trip duration.


Decision Tree Regression
------------------------
The max depth of the tree is chosen with :ref:`cross-label`. We also
calculate the residual and :ref:`cvscore-label` of the prediction.

Cross Validation
----------------

The following figure shows the plot for 5 consecutive :ref:`cvscore-label` for
a 5-fold :ref:`cross-label`. The mean of the scores is 0.83.

.. image:: _static/croosvalidationtimecost.png

Residual
--------
The residual of the prediction is 3.56 min.


Feature Importances
-------------------
The feature importances. The higher, the more important the feature. The importance of a feature is computed as the
(normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance

==============  ======
Categories      Value
==============  ======
PickLat         0.009
PickLng         0.012
DropLat         0.019
DropLng         0.014
Hour of day     0.057
Day of Week     0.020
Trip distance   0.870
==============  ======