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‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_images/sphx_glr_example_inspect_predictions_001.png

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‎development/_images/sphx_glr_example_multi_objective_001.png

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‎development/_sources/examples/20_basic/example_classification.rst.txt

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rank ensemble_weight type cost duration
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7 1 0.16 extra_trees 0.014184 1.732033
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16 2 0.02 gradient_boosting 0.021277 1.218025
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2 3 0.04 random_forest 0.028369 1.908905
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3 4 0.06 mlp 0.028369 1.089058
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19 5 0.06 extra_trees 0.028369 3.231020
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22 6 0.04 gradient_boosting 0.028369 1.288062
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31 7 0.08 extra_trees 0.028369 1.926138
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5 8 0.02 random_forest 0.035461 2.200523
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8 9 0.04 random_forest 0.035461 2.186472
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12 10 0.02 gradient_boosting 0.035461 1.463204
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18 11 0.02 random_forest 0.035461 2.230543
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30 12 0.10 liblinear_svc 0.042553 1.132402
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4 13 0.04 mlp 0.056738 1.515069
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28 14 0.08 mlp 0.056738 1.171671
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29 15 0.20 extra_trees 0.056738 1.984913
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32 16 0.02 extra_trees 0.106383 3.493422
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rank ensemble_weight type cost duration
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7 1 0.10 extra_trees 0.014184 1.843418
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31 2 0.04 extra_trees 0.014184 2.297438
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16 4 0.04 gradient_boosting 0.021277 1.309945
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21 3 0.02 extra_trees 0.021277 1.755215
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2 5 0.04 random_forest 0.028369 2.026494
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3 6 0.10 mlp 0.028369 1.258704
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6 8 0.02 mlp 0.028369 1.363868
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14 10 0.10 mlp 0.028369 2.544010
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19 9 0.06 extra_trees 0.028369 3.244506
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26 7 0.02 extra_trees 0.028369 2.627779
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5 13 0.02 random_forest 0.035461 2.428273
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17 12 0.04 gradient_boosting 0.035461 2.043105
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18 11 0.02 random_forest 0.035461 2.437218
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32 14 0.10 k_nearest_neighbors 0.035461 1.078064
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9 15 0.02 extra_trees 0.042553 2.279115
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15 17 0.02 mlp 0.049645 5.050552
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28 16 0.06 extra_trees 0.049645 2.382808
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4 18 0.04 mlp 0.056738 1.987111
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24 19 0.06 random_forest 0.070922 2.149439
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29 20 0.06 decision_tree 0.070922 1.320201
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20 21 0.02 passive_aggressive 0.078014 0.921303
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Accuracy score: 0.951048951048951
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Accuracy score: 0.958041958041958
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‎development/_sources/examples/20_basic/example_multilabel_classification.rst.txt

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2 1 1.0 random_forest 0.447294 4.07457
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‎development/_sources/examples/20_basic/example_multioutput_regression.rst.txt

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30 1 1.0 gaussian_process 7.252106e-09 4.515417
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2 1 0.92 random_forest 0.105487 2.587650
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R2 score: 0.9999999927998152
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R2 score: 0.8816516925708372
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‎development/_sources/examples/20_basic/example_regression.rst.txt

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25 1 0.38 sgd 0.436679 0.812176
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6 2 0.40 ard_regression 0.455042 0.839699
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27 3 0.02 ard_regression 0.462249 0.814517
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29 4 0.16 ard_regression 0.493390 0.767567
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11 5 0.02 random_forest 0.507400 10.729559
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7 6 0.02 gradient_boosting 0.518673 1.420055
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25 1 0.30 sgd 0.436679 0.843263
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33 2 0.36 liblinear_svr 0.443974 0.846886
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6 3 0.34 ard_regression 0.455042 0.872422
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Train R2 score: 0.5845958689841146
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Test R2 score: 0.3969087782855073
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Train R2 score: 0.5730992444209256
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Test R2 score: 0.40603183069422977
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‎development/_sources/examples/20_basic/sg_execution_times.rst.txt

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=================
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**06:10.926** total execution time for **examples_20_basic** files:
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+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 02:00.477 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:00.100 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 01:58.343 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:59.212 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:54.602 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:54.058 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:49.224 | 0.0 MB |
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| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:17.557 | 0.0 MB |
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‎development/_sources/examples/40_advanced/example_calc_multiple_metrics.rst.txt

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Accuracy score 0.9440559440559441
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################################################################################
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Metric results
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rank_test_scores param_classifier:__choice__ mean_test_score metric_balanced_accuracy metric_precision metric_recall metric_f1 metric_custom_error
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4 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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4 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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5 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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5 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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25 mlp 0.943262 0.935069 0.945055 0.966292 0.955556 0.056738
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15 random_forest 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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4 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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5 mlp 0.971631 0.961538 0.956989 1.000000 0.978022 0.028369
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1 extra_trees 0.985816 0.984767 0.988764 0.988764 0.988764 0.014184
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15 random_forest 0.964539 0.963915 0.977273 0.966292 0.971751 0.035461
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20 extra_trees 0.957447 0.954300 0.966292 0.966292 0.966292 0.042553
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4 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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4 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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5 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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5 random_forest 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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15 gradient_boosting 0.964539 0.963915 0.977273 0.966292 0.971751 0.035461
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4 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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4 mlp 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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23 mlp 0.950355 0.948682 0.965909 0.955056 0.960452 0.049645
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2 gradient_boosting 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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5 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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5 mlp 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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22 mlp 0.950355 0.948682 0.965909 0.955056 0.960452 0.049645
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3 gradient_boosting 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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15 gradient_boosting 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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15 random_forest 0.964539 0.959918 0.966667 0.977528 0.972067 0.035461
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4 extra_trees 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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29 passive_aggressive 0.921986 0.894231 0.890000 1.000000 0.941799 0.078014
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2 extra_trees 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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4 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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23 mlp 0.950355 0.940687 0.945652 0.977528 0.961326 0.049645
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28 random_forest 0.929078 0.923833 0.943820 0.943820 0.943820 0.070922
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5 extra_trees 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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28 passive_aggressive 0.921986 0.894231 0.890000 1.000000 0.941799 0.078014
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3 extra_trees 0.978723 0.975151 0.977778 0.988764 0.983240 0.021277
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5 gradient_boosting 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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22 mlp 0.950355 0.940687 0.945652 0.977528 0.961326 0.049645
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26 random_forest 0.929078 0.923833 0.943820 0.943820 0.943820 0.070922
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20 adaboost 0.957447 0.950303 0.956044 0.977528 0.966667 0.042553
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4 extra_trees 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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31 mlp 0.368794 0.500000 0.000000 0.000000 0.000000 0.631206
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25 mlp 0.943262 0.947061 0.976471 0.932584 0.954023 0.056738
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25 extra_trees 0.943262 0.931072 0.935484 0.977528 0.956044 0.056738
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20 liblinear_svc 0.957447 0.962295 0.988235 0.943820 0.965517 0.042553
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4 extra_trees 0.971631 0.969533 0.977528 0.977528 0.977528 0.028369
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30 extra_trees 0.893617 0.887748 0.920455 0.910112 0.915254 0.106383
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5 extra_trees 0.971631 0.965536 0.967033 0.988764 0.977778 0.028369
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30 mlp 0.368794 0.500000 0.000000 0.000000 0.000000 0.631206
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22 extra_trees 0.950355 0.952679 0.976744 0.943820 0.960000 0.049645
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26 decision_tree 0.929078 0.935825 0.975904 0.910112 0.941860 0.070922
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29 extra_trees 0.858156 0.807692 0.816514 1.000000 0.898990 0.141844
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1 extra_trees 0.985816 0.984767 0.988764 0.988764 0.988764 0.014184
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.. _sphx_glr_download_examples_40_advanced_example_calc_multiple_metrics.py:

‎development/_sources/examples/40_advanced/example_debug_logging.rst.txt

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/home/runner/work/auto-sklearn/auto-sklearn/autosklearn/data/target_validator.py:187: UserWarning: Fitting transformer with a pandas series which has the dtype category. Inverse transform may not be able preserve dtype when converting to np.ndarray
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2022-06-15 16:42:16,483 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-06-15 16:42:16.464126'), 'ensemble_optimization_score': 0.98989898989899, 'ensemble_test_score': 0.9924906132665833}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.06.15-16.42.16 for iteration 1.
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2022-06-15 17:13:31,595 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-06-15 17:13:31.576837'), 'ensemble_optimization_score': 0.47853535353535354, 'ensemble_test_score': 0.47434292866082606}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.06.15-17.13.31 for iteration 0.
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2022-06-15 17:13:36,440 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2022-06-15 17:13:36.420363'), 'ensemble_optimization_score': 0.9684343434343434, 'ensemble_test_score': 0.9824780976220275}], 50)/SingleThreadedClient() Started Ensemble builder job at 2022.06.15-17.13.36 for iteration 1.
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.. _sphx_glr_download_examples_40_advanced_example_debug_logging.py:

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