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": 16.84209895133972, "input_args": {"Data": " F1_l2 F9_l10 F1_l2+F9_l10 |F1_l2-F9_l10| F1_l2xF9_l10 F1_l2/F9_l10 F9_l10/F1_l2\n0 -0.621488 -0.327902 -0.949391 0.293586 0.203787 1.895347 0.527608\n1 -0.785875 -0.244125 -1.030000 0.541750 0.191852 3.219149 0.310641\n2 -1.836501 -0.124939 -1.961440 1.711563 0.229450 14.699214 0.068031\n3 -1.395929 -0.136677 -1.532606 1.259252 0.190792 10.213330 0.097911\n4 -0.943416 -0.207608 -1.151025 0.735808 0.195861 4.544213 0.220060\n... ... ... ... ... ... ... ...\n1594 -0.736966 -0.236572 -0.973538 0.500394 0.174345 3.115185 0.321008\n1595 -0.862496 -0.119186 -0.981683 0.743310 0.102798 7.236534 0.138188\n1596 -0.971431 -0.124939 -1.096370 0.846492 0.121369 7.775257 0.128613\n1597 -0.632629 -0.148742 -0.781371 0.483887 0.094098 4.253206 0.235117\n1598 -1.689660 -0.180456 -1.870116 1.509204 0.304909 9.363276 0.106800\n\n[1599 rows x 7 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, ...)"}}
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