[MRG+2] EXA Get rid of warnings in plot_svm_anova.py #11588
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TomDLT
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Jul 17, 2018
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LGTM for removing the warnings.
About the goal of this example, it seems that the non-informative features are not scaled as the original features, so they don't disturb to much the SVC. Moreover, the low number of samples make the feature selection choose some non-informative feature instead of the informative ones.
Increasing the number of samples (e.g. 500) and the noise scaling (e.g. 10) gives a nice maximum for low percentiles. Note that we could probably reduce the number of non-informative features (e.g. 64) for a nicer example.
GaelVaroquaux
approved these changes
Jul 17, 2018
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Fixes #11565
old example(master):
new example(my PC):
But I fail to understand the example. The example claims that "This example shows how to perform univariate feature selection ... to improve the classification scores", but we actually get highest accuracy with all the features (including the non-informative features added to the datasets)? If we select number of features equal to the original dataset, we only get 20%-30% accuracy through cross validation?