Downloads: 1
| # | dataset algorithm parameters seed accuracy f1_macro bal_accuracy roc_auc time | |||||||||||||||||||||||||||||||||||||||||||||||
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| 1 | geis_530.81 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=7016 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=7016 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=7016 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 7016 0.713302209504959 0.7133015405588519 0.7133046125062819 0.7790814115760594 27102.464614778997 |
| 2 | geis_530.81 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=11085 | 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=11085 | 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=11085 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 11085 0.7124416428217981 0.7124333903086386 0.7124511652186696 0.7797677496088135 29067.907099386997 |
| 3 | geis_530.81 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=4.832930238571752 | 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=4.832930238571752 | 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=4.832930238571752 | 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 0.7177986704244745 0.7177912714176085 0.717828578014918 0.7834757240284795 29967.998122510002 |
| 4 | geis_530.81 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=27164 | 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=27164 | 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=27164 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 27164 0.7138400636819345 0.7138399105675577 0.7138458814981385 0.7780705748316812 31368.730738906 |
| 5 | geis_530.81 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=30993 | 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=30993 | 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=30993 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 30993 0.7177126137561585 0.7177125714225174 0.7177158530272484 0.7852474377551368 31328.313832345 |
| 6 | geis_530.81 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=3674 | 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=3674 | 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=3674 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 3674 0.7151309137066758 0.7151254263511744 0.7151268390589058 0.7807917235998336 31676.835954585 |
| 7 | geis_530.81 ScaleLR memory=None | steps=[('scale' | StandardScaler(copy=True | with_mean=True | with_std=True)) | ('lr' | LogisticRegression(C=4.832930238571752 | 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=4.832930238571752 | 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=4.832930238571752 | 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 0.7147866870334115 0.7147845911691655 0.7147904965695766 0.7812232346333989 32171.024062735 |
| 8 | geis_530.81 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=9168 | 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=9168 | 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=9168 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 9168 0.7151739420408338 0.7151712837068984 0.7151711151336916 0.7804675009832263 32335.617927257 |
| 9 | geis_530.81 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 0.7184440954368452 0.7184388825086168 0.7184376576791706 0.7834033246297172 32548.052305206 |
| 10 | geis_530.81 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=1188 | 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=1188 | 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=1188 | lr__solver=saga | lr__tol=0.0001 | lr__verbose=0 | lr__warm_start=False 1188 0.7177986704244745 0.7177982608010217 0.7178064581960166 0.7822759803927612 35902.323397182 |