Downloads: 1
| # | dataset algorithm parameters seed accuracy f1_macro bal_accuracy roc_auc time | |||||||||||||||||||||||||||||||||||||||||||||||
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| 1 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.7847599703514611 | 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=0.7847599703514611 | 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=0.7847599703514611 | 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 0.8442150744119059 0.8439909886358006 0.8441058997732074 0.9173016746254614 22143.158841851 |
| 2 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.7847599703514611 | 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=0.7847599703514611 | 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=0.7847599703514611 | 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 0.8421614124926655 0.8419690882709834 0.842099217506469 0.9155122926815988 21843.565939501 |
| 3 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=1.623776739188721 | 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=1.623776739188721 | 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=1.623776739188721 | 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 0.8469355096815491 0.8467109709186904 0.8468726478182886 0.9190584260756934 22375.512879096 |
| 4 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=10.0 | 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=10.0 | 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=10.0 | 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 0.8469355096815491 0.8467756671035513 0.8469819332392928 0.9183675531402986 22504.184688629997 |
| 5 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=3.359818286283781 | 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=3.359818286283781 | 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=3.359818286283781 | 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 0.8426148183709393 0.8423947673677425 0.8423788084303439 0.9171622301777915 22839.956683944998 |
| 6 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.26366508987303583 | 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=0.26366508987303583 | 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=0.26366508987303583 | 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 0.8441083906758414 0.8439401683280494 0.8442019616078713 0.9174565895130586 22771.821643887 |
| 7 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=10.0 | 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=10.0 | 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=10.0 | 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 0.8429081986451166 0.8427072918450522 0.8430268383981869 0.9182612456010213 22859.552020529998 |
| 8 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=1.623776739188721 | 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=1.623776739188721 | 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=1.623776739188721 | 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 0.8432815917213421 0.8431049688577013 0.8433432659289435 0.917925208021695 23218.465988754997 |
| 9 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=0.5455594781168517 | 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=0.5455594781168517 | 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=0.5455594781168517 | 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 0.8433882754574065 0.8432117728271651 0.8433585435777271 0.9180505734931301 23693.730120852 |
| 10 | geis_250.00 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=1.1288378916846884 | 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=1.1288378916846884 | 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=1.1288378916846884 | 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 0.842534805568891 0.8423550742100854 0.8425330068565542 0.9156227106985868 32973.327275236006 |