@lists.sourceforge.net
Subject: Re: [Scikit-learn-general] getting different results with sklearn
gridsearchCV
As Laurent said using StandardScaler again is not necessary.
If you don't provide code for your custom grid-search, it is hard to say what
the difference might be ;)
Are the same parameters selected an
et
*Subject:* Re: [Scikit-learn-general] getting different results with
sklearn gridsearchCV
Hi Roberto.
GridSearchCV uses accuracy for selection if not other method is
specified, so there should be no difference.
Could you provide code?
Do you also create a pipeline when using your own grid searc
ilto:t3k...@gmail.com]
*Sent:* Friday, September 12, 2014 12:12 PM
*To:* scikit-learn-general@lists.sourceforge.net
*Subject:* Re: [Scikit-learn-general] getting different results with
sklearn gridsearchCV
Hi Roberto.
GridSearchCV uses accuracy for selection if not other method is
specified, s
with binning, I would just add the Binarizer to the pipeline, and right before
computing y_predictions.
Is there anything wrong with what I'm doing?
Thank you
From: Andy [mailto:t3k...@gmail.com]
Sent: Friday, September 12, 2014 12:12 PM
To: scikit-learn-general@lists.sourceforge.net
Sub
Hi Roberto.
GridSearchCV uses accuracy for selection if not other method is
specified, so there should be no difference.
Could you provide code?
Do you also create a pipeline when using your own grid search? I would
imagine there is some difference in how you do the fitting in the pipeline.
Regarding my previous question, I suspect the difference lies in the scoring
function.
What is the default scoring function used by gridsearch?
In my own implementation I am using
number of correctly classified samples (no weighting) / total number of samples
sklearn gridsearch function must b
I am comparing the results of sklearn cross-validation and my own cross
validation.
I tested linearSVC under the following conditions:
- Data scaling per grid search
- Data scaling + 2-level quantization, per grid search
Specifically, I have done the following:
Sklearn gridSe
