I think all those results correspond to the RBF kernel. You have far too few samples to learn an RBF model, so it's stored trivial coefficients independent of C and gamma.
On Thu, Aug 1, 2013 at 1:56 PM, Josh Wasserstein <[email protected]>wrote: > Hi, > > I am noticing that for some models in my grid search I get virtually the > same exact results across 100 repetitions of CV. Is this normal? In case it > matters, I am working with ~30 data points (I know, it's a small dataset) > with ~5 dimensions. > > Below are the details of the configuration that I used for grid search: > > with K=4: > sfs = StratifiedShuffleSplit(y,n_iter=100,test_size=1.0/K) > > I am working on a 3-labels classification problem with the following SVM > kernels: > > tuned_parameters = [ > {'kernel': ['linear'], 'C': > np.power(2,np.arange(-8.,8., step_size)}, > {'kernel': ['rbf'], 'C': > np.power(2,np.arange(-8.,8., step_size), 'gamma': > np.power(2,np.arange(-8.,8., step_size)}, > ] > > #....# > clf = GridSearchCV(SVC(C=1, cache_size=5000), > tuned_parameters, > scoring=f1_macro, > verbose=1, n_jobs=1, cv=sfs) > clf.fit(X,y) > #....# > > Below is the plotting of the *cv_validation_scores* (mean, min, max, > mean-std and mean+std) from *clf.cv_scores* > > More specifically: > > all_scores = [x.cv_validation_scores for x in clf.cv_scores_] > all_scores = np.vstack(all_scores).transpose() > > # Load the scores in a dataframe in pandas and sort the columns (the > models) > all_scores_df = pd.DataFrame(all_scores) > sorted_columns = all_scores_df.mean().order(ascending=False).index > sorted_scores = all_scores_df.reindex_axis(sorted_columns, axis=1) > > # Plot envelope: > max_values = sorted_scores.max().values > min_values = sorted_scores.min().values > mean_values = sorted_scores.mean().values > std_values = sorted_scores.std().values > > fig = plt.figure() > fig.hold(True) > plt.plot(max_values, color='r') > plt.plot(min_values, color='r') > plt.plot(mean_values, color='b') > > above = mean_values + std_values > above = np.minimum(above,max_values) > plt.plot(above, color='g', linestyle='--', linewidth=2.0) > below = mean_values - std_values > below = np.maximum(below,min_values) > plt.plot(below, color='g', linestyle='--', linewidth=2.0) > > [image: Inline image 1] > And here is an example of one of those models: > > > clf.cv_scores_[8].cv_validation_scores > > array([ 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376, > 0.21505376, 0.21505376, 0.21505376, 0.21505376, 0.21505376]) > > Thanks, > > Josh > > > ------------------------------------------------------------------------------ > Get your SQL database under version control now! > Version control is standard for application code, but databases havent > caught up. So what steps can you take to put your SQL databases under > version control? Why should you start doing it? Read more to find out. > http://pubads.g.doubleclick.net/gampad/clk?id=49501711&iu=/4140/ostg.clktrk > _______________________________________________ > Scikit-learn-general mailing list > [email protected] > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general > >
<<f1_macro_envelope.jpg>>
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