FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
				https://doi.org/10.1109/TVCG.2022.3141040
			
			
		
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				{"duration": 21.604820013046265, "input_args": {"Data": "         F1     F2       F3   F4     F5  F6_b   F7    F8    F9   F10   F11\n0     0.650  0.065  0.99460  7.3   21.0     1  1.2  3.39  0.47  15.0  10.0\n1     0.580  0.073  0.99680  7.8   18.0     1  2.0  3.36  0.57   9.0   9.5\n2     0.280  0.092  0.99690  8.5  103.0    11  1.8  3.30  0.75  35.0  10.5\n3     0.380  0.066  0.99680  8.1   30.0     6  2.1  3.23  0.73  13.0   9.7\n4     0.520  0.085  0.99680  7.5   35.0     3  1.9  3.38  0.62  12.0   9.5\n...     ...    ...      ...  ...    ...   ...  ...   ...   ...   ...   ...\n1594  0.600  0.090  0.99490  6.2   44.0     2  2.0  3.45  0.58  32.0  10.5\n1595  0.550  0.062  0.99512  5.9   51.0     2  2.2  3.52  0.76  39.0  11.2\n1596  0.510  0.076  0.99574  6.3   40.0     3  2.3  3.42  0.75  29.0  11.0\n1597  0.645  0.075  0.99547  5.9   44.0     3  2.0  3.57  0.71  32.0  10.2\n1598  0.310  0.067  0.99549  6.0   42.0     9  3.6  3.39  0.66  18.0  11.0\n\n[1599 rows x 11 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n              colsample_bynode=1, colsample_bytree=0.9223705789444759,\n              eta=0.08487346516301046, gamma=0, gpu_id=-1,\n              importance_type='gain', interaction_constraints='',\n              learning_rate=0.0848734677, max_delta_step=0, max_depth=7,\n              min_child_weight=1, missing=nan, monotone_constraints='()',\n              n_estimators=76, n_jobs=12, num_parallel_tree=1,\n              objective='multi:softprob', probability=True, random_state=42,\n              reg_alpha=0, reg_lambda=1, scale_pos_weight=None, silent=True,\n              subsample=0.8912139968434072, tree_method='exact',\n              use_label_encoder=False, validate_parameters=1, ...)"}} |