Dear All,
I am interested in attempting model selection with GridSearchCV() on
GradientBoostingRegressor(). I am quite new to boosting but I see from
the nice examples in sklearn documentation [0] that once the n_estimator
is fixed, it is possible to evaluate the classifiers at each boosting
iteration through GradientBoostingRegressor.staged_decision_function()
and similar things (oob_score_, staged_predict).
As in the figures of the examples the score on the test set, e.g. deviance,
has sometimes a minimum and it would be nice to get it during model selection
in order to score the given set of parameter values on it. How to do that within
GridSearchCV?
What I would like to do is to define sets of GradientBoosting parameter
values, e.g.
{'learn_rate':[0.05, 0.01, 0.001], 'subsample':[0.25, 0.5, 0.75], ...ecc.}
and then to do grid search to decide which set of values gives the minimum
score, e.g. mse, in the minimum of the related graph "score vs boosting
iteration".
Moreover it would be great to keep track of at which boosting iteration
this minimum occurs.
I am reading the documentation but I cannot understand how to do that. Could
you help me?
Best,
Emanuele
[0]:
http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#example-ensemble-plot-gradient-boosting-regression-py
http://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html#example-ensemble-plot-gradient-boosting-regularization-py
------------------------------------------------------------------------------
Live Security Virtual Conference
Exclusive live event will cover all the ways today's security and
threat landscape has changed and how IT managers can respond. Discussions
will include endpoint security, mobile security and the latest in malware
threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/
_______________________________________________
Scikit-learn-general mailing list
Scikit-learn-general@lists.sourceforge.net
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
