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": 1.665640115737915, "input_args": {"Data": " F1_p2 F2_l1p F1+F2 |F1-F2| F1xF2 F1/F2 F2/F1\n0 6.25 1.458615 7.708615 4.791385 9.116344 4.284887 0.233378\n1 3.61 1.308333 4.918333 2.301667 4.723081 2.759237 0.362419\n2 4.41 1.386294 5.796294 3.023706 6.113558 3.181143 0.314352\n3 3.24 1.360977 4.600977 1.879023 4.409564 2.380644 0.420054\n4 4.84 1.386294 6.226294 3.453706 6.709665 3.491322 0.286424\n.. ... ... ... ... ... ... ...\n145 0.09 1.386294 1.476294 1.296294 0.124766 0.064921 15.403271\n146 0.04 1.568616 1.608616 1.528616 0.062745 0.025500 39.215398\n147 0.04 1.435085 1.475085 1.395085 0.057403 0.027873 35.877113\n148 0.04 1.547563 1.587563 1.507563 0.061903 0.025847 38.689063\n149 0.04 1.458615 1.498615 1.418615 0.058345 0.027423 36.465376\n\n[150 rows x 7 columns]", "clf": "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n colsample_bynode=1, colsample_bytree=1, eta=0.17855860960340292,\n gamma=0, gpu_id=-1, importance_type='gain',\n interaction_constraints='', learning_rate=0.178558603,\n max_delta_step=0, max_depth=9, min_child_weight=1, missing=nan,\n monotone_constraints='()', n_estimators=14, n_jobs=12,\n num_parallel_tree=1, objective='multi:softprob', probability=True,\n random_state=42, reg_alpha=0, reg_lambda=1, scale_pos_weight=None,\n silent=True, subsample=1, tree_method='exact',\n use_label_encoder=False, validate_parameters=1, ...)"}} |