[CV] END max_features=1.0, min_samples_split=2, n_estimators=25; total time= 1.8s +[CV] END max_features=1.0, min_samples_split=2, n_estimators=25; total time= 1.9s
[CV] END max_features=1.0, min_samples_split=2, n_estimators=25; total time= 2.2s +[CV] END max_features=1.0, min_samples_split=2, n_estimators=25; total time= 1.8s
[CV] END max_features=1.0, min_samples_split=5, n_estimators=100; total time= 6.8s +[CV] END max_features=1.0, min_samples_split=5, n_estimators=100; total time= 6.9s
[CV] END max_features=1.0, min_samples_split=5, n_estimators=100; total time= 6.8s +[CV] END max_features=1.0, min_samples_split=5, n_estimators=100; total time= 6.9s
[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 2.1s +[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 2.0s
[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 2.1s +[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 2.0s
[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 2.1s +[CV] END max_features=sqrt, min_samples_split=5, n_estimators=100; total time= 2.0s
<matplotlib.collections.PathCollection at 0x7fc2b5a66ad0>+
<matplotlib.collections.PathCollection at 0x7faf043ab760>
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, n_estimators=150; total time= 0.7s +[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, n_estimators=150; total time= 0.6s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, n_estimators=150; total time= 0.7s +[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=1, n_estimators=150; total time= 0.6s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=5, n_estimators=150; total time= 0.7s +[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=5, n_estimators=150; total time= 0.6s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=5, n_estimators=150; total time= 0.7s +[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=5, n_estimators=150; total time= 0.6s
[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=5, n_estimators=150; total time= 0.7s +[CV] END max_depth=2, max_features=sqrt, min_samples_leaf=5, n_estimators=150; total time= 0.6s
[CV] END max_depth=2, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 1.0s +[CV] END max_depth=2, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 0.9s
[CV] END max_depth=2, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 1.0s +[CV] END max_depth=2, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 0.9s
[CV] END max_depth=2, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 1.0s +[CV] END max_depth=2, max_features=1.0, min_samples_leaf=1, n_estimators=100; total time= 0.9s
[flaml.automl.logger: 04-09 16:13:26] {1680} INFO - task = classification
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-/opt/hostedtoolcache/Python/3.10.14/x64/lib/python3.10/site-packages/sklearn/linear_model/_sag.py:350: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge
- warnings.warn(
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[flaml.automl.logger: 04-09 16:14:28] {2628} INFO - retrain lgbm for 0.8s
+[flaml.automl.logger: 04-09 16:14:52] {2628} INFO - retrain lgbm for 0.7s
[flaml.automl.logger: 04-09 16:14:28] {2631} INFO - retrained model: LGBMClassifier(colsample_bytree=0.6782006405163307,
+[flaml.automl.logger: 04-09 16:14:52] {2631} INFO - retrained model: LGBMClassifier(colsample_bytree=0.6782006405163307,
learning_rate=0.08222714104485472, max_bin=511,
min_child_samples=5, n_estimators=1, n_jobs=-1, num_leaves=254,
reg_alpha=0.003934614746573571, reg_lambda=0.003560646472734122,
@@ -11329,14 +11223,14 @@ AutoML with auto-sklearn
-[flaml.automl.logger: 04-09 16:14:28] {1931} INFO - fit succeeded
+[flaml.automl.logger: 04-09 16:14:52] {1931} INFO - fit succeeded
[flaml.automl.logger: 04-09 16:14:28] {1932} INFO - Time taken to find the best model: 48.25734806060791
+[flaml.automl.logger: 04-09 16:14:52] {1932} INFO - Time taken to find the best model: 47.191701889038086