FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches
https://doi.org/10.1109/TVCG.2022.3141040
You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
1 lines
3.7 KiB
1 lines
3.7 KiB
{"duration": 25.673484086990356, "input_args": {"Data": " F1 F2 F3 F4 F5 F6 F7 F8 F9 F10 F11 F12 F13 F14 F15 F16 F17 F18 F19 F20 F21 F22 F23 F24 F25 F26 F27 F28 F29 F30 F31 F32 F33 F34 F35 F36 F37 F38 F39 F40 F41\n0 1.185 9.085 0 0 2.263 3 0 3.642 0.000 0 1 9.902 46.7 1 3.1934 1.359 0 1.034 1.476 3 3 0 0 1.100 2.483 4.825 0 0 2 0 0.000 1 0 0 0.014 0 4.054 0 0 0 0\n1 0.000 8.179 2 0 2.194 0 6 3.526 0.000 6 0 10.054 35.1 0 1.8929 1.209 0 0.974 4.130 0 0 0 0 1.139 1.744 4.562 0 0 0 0 0.000 2 0 0 0.000 0 3.489 0 4 0 0\n2 0.762 8.297 0 3 2.424 0 0 3.339 21.884 0 0 10.226 42.1 0 2.3934 1.204 0 1.027 1.027 0 0 0 2 1.120 2.773 4.982 0 0 0 8 -0.686 3 0 0 0.004 0 3.693 0 0 0 0\n3 1.747 9.673 0 2 2.690 23 0 4.645 9.855 0 1 12.353 31.6 0 7.7233 0.906 0 1.291 5.094 0 0 0 0 1.348 5.741 6.316 1 0 0 2 -4.617 0 0 11 0.000 0 3.993 0 0 0 1\n4 1.824 9.825 0 2 2.700 27 0 4.795 9.894 0 1 12.519 31.8 0 7.9184 0.906 0 1.292 5.891 0 0 0 0 1.350 5.742 6.330 1 0 0 2 -4.724 0 0 13 0.000 0 4.005 0 0 0 1\n.. ... ... .. .. ... .. .. ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...\n832 1.125 7.878 0 1 2.136 0 0 2.990 0.000 0 0 9.311 43.8 0 3.0778 1.253 0 0.991 1.481 1 0 0 0 1.117 2.146 4.499 0 0 1 2 0.000 1 0 0 0.045 0 3.573 0 0 0 0\n833 1.187 8.046 1 1 2.222 0 2 3.105 0.000 2 0 9.668 38.9 0 3.2726 1.230 0 0.998 1.358 2 0 0 0 1.132 2.315 4.783 2 0 2 2 0.000 1 0 0 -0.025 0 3.666 0 2 0 0\n834 0.625 8.901 0 2 2.499 0 0 3.745 24.203 0 0 10.681 58.3 0 2.3715 1.312 0 1.008 1.262 4 0 0 0 1.087 2.500 5.310 0 0 0 8 -0.128 6 0 0 0.000 0 3.942 0 0 0 0\n835 3.866 8.778 0 6 2.361 0 0 4.201 11.747 0 1 10.735 32.4 0 1.9452 1.166 0 0.992 10.593 0 0 0 0 1.140 2.300 4.977 3 0 0 9 -0.347 1 0 0 0.000 0 3.497 0 0 0 0\n836 3.706 8.680 0 6 2.361 0 0 4.127 11.724 0 1 10.694 31.4 0 1.9472 1.153 0 0.993 9.639 0 0 0 0 1.143 2.321 4.977 3 0 0 9 -0.338 1 0 0 0.000 0 3.497 0 0 0 0\n\n[837 rows x 41 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)"}} |