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": 10.513028860092163, "input_args": {"Data": " F2 F37_l2 F2+F37_l2 |F2-F37_l2| F2xF37_l2 F2/F37_l2 F37_l2/F2\n0 9.085 2.019346 11.104346 7.065654 18.345759 4.498981 0.222273\n1 8.179 1.802814 9.981814 6.376186 14.745212 4.536797 0.220420\n2 8.297 1.884793 10.181793 6.412207 15.638130 4.402074 0.227166\n3 9.673 1.997473 11.670473 7.675527 19.321557 4.842618 0.206500\n4 9.825 2.001802 11.826802 7.823198 19.667707 4.908077 0.203746\n.. ... ... ... ... ... ... ...\n832 7.878 1.837136 9.715136 6.040864 14.472957 4.288197 0.233198\n833 8.046 1.874207 9.920207 6.171793 15.079868 4.293016 0.232936\n834 8.901 1.978928 10.879928 6.922072 17.614436 4.497890 0.222326\n835 8.778 1.806118 10.584118 6.971882 15.854102 4.860148 0.205755\n836 8.680 1.806118 10.486118 6.873882 15.677102 4.805888 0.208078\n\n[837 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n colsample_bynode=1, colsample_bytree=0.8702434039276976,\n eta=0.2593099998788648, gamma=0, gpu_id=-1,\n importance_type='gain', interaction_constraints='',\n learning_rate=0.259310007, max_delta_step=0, max_depth=6,\n min_child_weight=1, missing=nan, monotone_constraints='()',\n n_estimators=188, n_jobs=12, num_parallel_tree=1,\n probability=True, random_state=42, reg_alpha=0, reg_lambda=1,\n scale_pos_weight=1, silent=True, subsample=0.9972633684544026,\n tree_method='exact', use_label_encoder=False,\n validate_parameters=1, verbosity=0)"}} |