| @@ 14-25 (lines=12) @@ | ||
| 11 | X, y = data.data, data.target |
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| 12 | ||
| 13 | ||
| 14 | def model_etc(opt): |
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| 15 | etc = ExtraTreesClassifier( |
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| 16 | n_estimators=opt["n_estimators"], |
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| 17 | criterion=opt["criterion"], |
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| 18 | max_features=opt["max_features"], |
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| 19 | min_samples_split=opt["min_samples_split"], |
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| 20 | min_samples_leaf=opt["min_samples_leaf"], |
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| 21 | bootstrap=opt["bootstrap"], |
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| 22 | ) |
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| 23 | scores = cross_val_score(etc, X, y, cv=3) |
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| 24 | ||
| 25 | return scores.mean() |
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| 26 | ||
| 27 | ||
| 28 | def model_rfc(opt): |
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| @@ 14-25 (lines=12) @@ | ||
| 11 | X, y = data.data, data.target |
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| 12 | ||
| 13 | ||
| 14 | def model_etc(opt): |
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| 15 | etc = ExtraTreesClassifier( |
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| 16 | n_estimators=opt["n_estimators"], |
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| 17 | criterion=opt["criterion"], |
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| 18 | max_features=opt["max_features"], |
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| 19 | min_samples_split=opt["min_samples_split"], |
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| 20 | min_samples_leaf=opt["min_samples_leaf"], |
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| 21 | bootstrap=opt["bootstrap"], |
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| 22 | ) |
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| 23 | scores = cross_val_score(etc, X, y, cv=3) |
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| 24 | ||
| 25 | return scores.mean() |
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| 26 | ||
| 27 | ||
| 28 | def model_rfc(opt): |
|