ENH Improve speed plot_adaboost_multiclass.py #21651
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adrinjalali
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Nov 13, 2021
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| n_samples=13000, n_features=10, n_classes=3, random_state=1 | ||
| ) | ||
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| n_split = 3000 | ||
| n_split = 2000 |
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this is not reducing the number of splits, it's the size of the training set. You can instead make the size of the training set also smaller, and use train_test_split to get like 50% for training.
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How does this then have an impact on the execution time if it's just the relation train to test?
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I changed it back from 2000 to the original 3000. Even with only the n_estimators parameters changed, time reduction is from 16 sec to 7 sec
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Is it still reproducing the aforementioned figure from the original article? In terms of required input for instance. |
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@adrinjalali Yes, still looking extremely similar |
adrinjalali
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Nov 16, 2021
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Thanks @sveneschlbeck ! |
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#21598 @TomDLT @adrinjalali
Tuned down the estimator number a bit, also decreased the number of splits.
