MNT speed up plot_ensemble_oob.py #21730
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…ntime by almost 50%
glemaitre
reviewed
Nov 22, 2021
| @@ -81,7 +81,7 @@ | |||
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| # Range of `n_estimators` values to explore. | |||
| min_estimators = 15 | |||
| max_estimators = 175 | |||
| max_estimators = 125 | |||
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| for label, clf in ensemble_clfs: | |||
| for i in range(min_estimators, max_estimators + 1): | |||
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We might not need to increase only 1 tree at a time but maybe 5. This should speed up the process since we will not call as many times the Python for a loop even if we have to train the same number of trees.
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Changed estimator range to (15, 150, 5) |
adrinjalali
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Nov 24, 2021
glemaitre
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Nov 29, 2021
* After 125, more estimators does not help the model and it improves runtime by almost 50% * accelerate plot_ensemble_oob.py changed max_estimator to 150 with step=5
samronsin
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Nov 30, 2021
* After 125, more estimators does not help the model and it improves runtime by almost 50% * accelerate plot_ensemble_oob.py changed max_estimator to 150 with step=5
glemaitre
pushed a commit
to glemaitre/scikit-learn
that referenced
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Dec 24, 2021
* After 125, more estimators does not help the model and it improves runtime by almost 50% * accelerate plot_ensemble_oob.py changed max_estimator to 150 with step=5
glemaitre
pushed a commit
that referenced
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Dec 25, 2021
* After 125, more estimators does not help the model and it improves runtime by almost 50% * accelerate plot_ensemble_oob.py changed max_estimator to 150 with step=5
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After 125, more estimators does not help the models and reducing max_estimators to 125 improves runtime by almost 50%
Reference Issues/PRs
#21598
What does this implement/fix? Explain your changes.
Any other comments?