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.
Cheers,
Andy
On 09/12/2014 05:09 PM, Pagliari, Roberto wrote:
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 be using something else, or maybe the
same, but with weighting?
Thanks,
*From:* Pagliari, Roberto
*Sent:* Friday, September 12, 2014 10:21 AM
*To:* 'scikit-learn-general@lists.sourceforge.net'
*Subject:* getting different results with sklearn gridsearchCV
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 gridSearchCV
-Create a pipeline with [StandardScaler, LinearSVC] if no binning is
used, or [StandardScaler, Binarizer, LinearSVC], if binning is used
-Invoke sklearn gridsearch (only C is provided as a parameter to
optimize over)
-When done with gridsearch,
oScale entire training set
oScale test set (with mean/std found on training set)
oQuantize, if quantization is used
o run LinearSVC, with best C value found
My own grid search
-Search over all possible values of C (same range as above)
-For each value of C, use stratifiedKFold with random_seed set to a
random number
oScale train cross-validation datased, and test cross validation
dataset with train cv mean and std
oIf binning is used, apply binary binning (my own function), on top of
StandardScaler
oFor each value of C compute average score over all partition, where
the score is defined as number of correctly classified samples / total
number of samples
-When done with gridsearch,
oScale entire training set
oScale test set (with mean/std found on training set)
oQuantize, if quantization is used
o run LinearSVC, with best C value found
For some reason, I’m getting different results. In particular, sklearn
gridsearch performs better than my own gridsearch when not using
quantization, and it gets worse with quantization. With my own
gridsearch I’m getting the opposite trend.
Is my understanding of sklearn gridsearch wrong, or are there any
issues with it?
Thank you,
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