On 02/11/2015 04:22 PM, Timothy Vivian-Griffiths wrote:
Hi Gilles,
Thank you so much for clearing this up for me. So, am I right in thinking
that the feature selection is carried for every CV-fold, and then once the
best parameters have been found, the pipeline is then run on the whole
On 11 Feb 2015, at 16:31, Andy t3k...@gmail.com wrote:
On 02/11/2015 04:22 PM, Timothy Vivian-Griffiths wrote:
Hi Gilles,
Thank you so much for clearing this up for me. So, am I right in thinking
that the feature selection is carried for every CV-fold, and then once the
best
Hi Gilles,
Thank you so much for clearing this up for me. So, am I right in thinking that
the feature selection is carried for every CV-fold, and then once the best
parameters have been found, the pipeline is then run on the whole training set
in order to get the .best_estimator_?
One final
You could use
grid2.best_estimator_.named_steps['feature_selection'].get_support(),
or .transform(feature_names) instead of .get_support(). Note for instance
that if you have a pipeline of multiple feature selectors, for some reason,
.transform(feature_names) remains useful while .get_support()
On 11 February 2015 at 22:22, Timothy Vivian-Griffiths
vivian-griffith...@cardiff.ac.uk wrote:
Hi Gilles,
Thank you so much for clearing this up for me. So, am I right in thinking
that the feature selection is carried for every CV-fold, and then once the
best parameters have been found, the
Hi Tim,
On 9 February 2015 at 19:54, Timothy Vivian-Griffiths
vivian-griffith...@cardiff.ac.uk wrote:
Just a quick follow up to some of the previous problems that I have had:
after getting some kind assistance at the PyData London meetup last week, I
found out why I was getting different
Just a quick follow up to some of the previous problems that I have had: after
getting some kind assistance at the PyData London meetup last week, I found out
why I was getting different results using an SVC in R, and it was happening
because R scales the inputs automatically whereas sklearn
