Downloads: 1
| # | dataset algorithm parameters seed accuracy f1_macro bal_accuracy roc_auc time | |||||||||||||||||||||||||||||||||||||||||||||||
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| 1 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.18329807108324356 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=11085 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.18329807108324356 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=11085 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.18329807108324356 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=11085 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 11085 0.7439516129032258 0.7438256166249821 0.7438321570301265 0.8067559945732535 702.244114461 |
| 2 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=3674 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=3674 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.12742749857031335 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=3674 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 3674 0.7535282258064516 0.7534514975866773 0.7534412585174006 0.8255992032641779 636.844978095 |
| 3 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=14899 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=14899 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=14899 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 14899 0.7429435483870968 0.7425657934908714 0.7425051459937486 0.8216523087088001 771.745135393 |
| 4 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=16612 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=16612 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.12742749857031335 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=16612 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 16612 0.75 0.7499837398373983 0.7505904721235137 0.8281586318738021 684.0391832439999 |
| 5 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.18329807108324356 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=30993 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.18329807108324356 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=30993 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.18329807108324356 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=30993 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 30993 0.75 0.7499349466562582 0.7502080828632203 0.8212956931166494 716.505880792 |
| 6 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=4180 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=4180 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=4180 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 4180 0.7464717741935484 0.7464717097850622 0.7465053886373684 0.8230199742890099 712.8310334409999 |
| 7 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=1188 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=1188 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=1188 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 1188 0.7459677419354839 0.7459654185995701 0.7460930155506975 0.822128924134833 730.3912500780001 |
| 8 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=7016 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=7016 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=7016 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 7016 0.7610887096774194 0.7610593296562911 0.7613342548174649 0.8309710139035693 839.6101421760001 |
| 9 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=27164 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=27164 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.12742749857031335 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=27164 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 27164 0.7555443548387096 0.755499072932291 0.7554896811879337 0.8228993483925253 1154.4142147779999 |
| 10 | geis_331.0 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=9168 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=9168 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.12742749857031335 | lr__class_weight=None | lr__dual=False | lr__fit_intercept=True | lr__intercept_scaling=1 | lr__max_iter=1000 | lr__multi_class=ovr | lr__n_jobs=1 | lr__penalty=l1 | lr__random_state=9168 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 9168 0.7520161290322581 0.7520151210292055 0.7520364860250719 0.8300773945986374 1059.681426452 |