Downloads: 1
| # | dataset algorithm parameters seed accuracy f1_macro bal_accuracy roc_auc time | |||||||||||||||||||||||||||||||||||||||||||||||
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| 1 | geis_571.8 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 0.8247989843419382 0.8247964427811869 0.8248080430636886 0.9027348938237075 830.224352929 |
| 2 | geis_571.8 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.06158482110660264 | 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=0.06158482110660264 | 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=0.06158482110660264 | 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 0.8281845112145577 0.8280844795825479 0.8280254343445346 0.901623578580458 854.694382623 |
| 3 | geis_571.8 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 0.8235294117647058 0.8235282740059384 0.8235464787239073 0.9007175371054655 843.7371921719999 |
| 4 | geis_571.8 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=3674 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=3674 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | 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=3674 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 3674 0.8176047397376217 0.8175826553489258 0.8176900207751272 0.8973006662368364 1098.1704574920002 |
| 5 | geis_571.8 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=30993 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=30993 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | 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=30993 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 30993 0.8146424037240796 0.8142615643035576 0.8140879840628314 0.8985130564393614 898.498294298 |
| 6 | geis_571.8 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=11085 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=11085 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | 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=11085 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 11085 0.8214134574693187 0.8213486680865336 0.8213189623675557 0.9019729630982828 1229.039646964 |
| 7 | geis_571.8 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 0.8044858231062209 0.8044855079731221 0.8045023289266879 0.8808020791992676 1184.8556409690002 |
| 8 | geis_571.8 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=9168 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=9168 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | 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=9168 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 9168 0.8137960220059247 0.8137885185466436 0.8138219681502654 0.889340439706862 1337.711283958 |
| 9 | geis_571.8 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=4180 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=4180 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.12742749857031335 | 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=4180 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 4180 0.8247989843419382 0.8246896934351775 0.8246515389883742 0.9015941518155218 1249.097131745 |
| 10 | geis_571.8 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=16612 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.18329807108324356 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=16612 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.18329807108324356 | 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=16612 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 16612 0.7917900973338976 0.791758733051904 0.7917548401701529 0.8731440581923633 1483.817441182 |