@@ 28-39 (lines=12) @@ | ||
25 | return scores.mean() |
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26 | ||
27 | ||
28 | def model_rfc(opt): |
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29 | rfc = RandomForestClassifier( |
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30 | n_estimators=opt["n_estimators"], |
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31 | criterion=opt["criterion"], |
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32 | max_features=opt["max_features"], |
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33 | min_samples_split=opt["min_samples_split"], |
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34 | min_samples_leaf=opt["min_samples_leaf"], |
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35 | bootstrap=opt["bootstrap"], |
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36 | ) |
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37 | scores = cross_val_score(rfc, X, y, cv=3) |
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38 | ||
39 | return scores.mean() |
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40 | ||
41 | ||
42 | def model_gbc(opt): |
@@ 28-39 (lines=12) @@ | ||
25 | return scores.mean() |
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26 | ||
27 | ||
28 | def model_rfc(opt): |
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29 | rfc = RandomForestClassifier( |
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30 | n_estimators=opt["n_estimators"], |
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31 | criterion=opt["criterion"], |
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32 | max_features=opt["max_features"], |
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33 | min_samples_split=opt["min_samples_split"], |
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34 | min_samples_leaf=opt["min_samples_leaf"], |
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35 | bootstrap=opt["bootstrap"], |
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36 | ) |
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37 | scores = cross_val_score(rfc, X, y, cv=3) |
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38 | ||
39 | return scores.mean() |
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40 | ||
41 | ||
42 | def model_gbc(opt): |