Downloads: 1
| # | dataset algorithm parameters seed accuracy f1_macro bal_accuracy roc_auc time | |||||||||||||||||||||||||||||||||||||||||||||||
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| 1 | geis_250.40 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 0.9079774908970539 0.9079480792785684 0.908031193539342 0.9571386323400842 2902.4283406500003 |
| 2 | geis_250.40 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=7016 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.18329807108324356 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=7016 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.18329807108324356 | 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=7016 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 7016 0.9074809665673618 0.9074580882418015 0.9076248397306126 0.9588018506601056 2309.503862349 |
| 3 | geis_250.40 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 0.9069844422376696 0.9069513170390364 0.9071389906728848 0.9630974087450471 2397.523241543 |
| 4 | geis_250.40 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=16612 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.08858667904100823 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=16612 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.08858667904100823 | 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=16612 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 16612 0.9107911287653095 0.9107587992164645 0.9107621217354107 0.9607320113393911 2940.3623998790003 |
| 5 | geis_250.40 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.37926901907322497 | 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=0.37926901907322497 | 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=0.37926901907322497 | 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 0.9023502151605428 0.902303585200094 0.9022986887348616 0.9581972123220419 2882.8816881440002 |
| 6 | geis_250.40 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 0.9131082423038729 0.9130635449699269 0.9130606048918346 0.9608175936339355 3102.416431592 |
| 7 | geis_250.40 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 0.9031777557100298 0.9031462341236749 0.9031900948858793 0.9556504909856935 3621.7347135910004 |
| 8 | geis_250.40 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=11085 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False))] | scale=StandardScaler(copy=True | with_mean=True | with_std=True) | lr=LogisticRegression(C=0.12742749857031335 | class_weight=None | dual=False fit_intercept=True | intercept_scaling=1 | max_iter=1000 multi_class='ovr' | n_jobs=1 | penalty='l1' | random_state=11085 solver='saga' | tol=0.0001 | verbose=0 | warm_start=False) | scale__copy=True | scale__with_mean=True | scale__with_std=True | lr__C=0.12742749857031335 | 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=11085 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 11085 0.9094670638861304 0.9094058939810366 0.9093396069287142 0.9611746774080436 4463.262982641 |
| 9 | geis_250.40 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.37926901907322497 | 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=0.37926901907322497 | 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=0.37926901907322497 | 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 0.9054948692485931 0.9054886531961452 0.9056091051502578 0.9594642157408486 3305.601914107 |
| 10 | geis_250.40 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 0.9126117179741807 0.9125080287967089 0.9122878498046825 0.9646663796946586 4355.554292494 |