Add a readme with logo and example #3
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added 17 commits
February 28, 2018 12:14
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LGTM +1 |
agramfort
reviewed
Mar 2, 2018
| X_train, y_train = X[:len(y)//2], y[:len(y)//2] | ||
| X_test, y_test = X[len(y)//2:], y[len(y)//2:] | ||
| clf.fit(X_train, y_train) | ||
| y_score = clf.score_top_rules(X_test) # Get a risk score for each test example |
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why not using the standard method score_samples from sklearn API? this score_top_rules name makes the API very specific to the estimator which defeats the API consistency of sklearn.
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we have 3 different scoring methods: decision_function, rules_vote, score_top_rules.
Maybe we should add a class parameter to chose one of these 3 functions at initialization ?
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hum ok. Is that document somewhere when to use which?
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I added two examples and a short description about the tradeoff between interpretability and performances