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Soheila Farokhi 5 days ago
ran flow sklearn.pipeline.Pipeline(Prep ... est.RandomForestClassifier)(5) on task Supervised Classification on data set credit-g
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.7562, f_measure: 0.7228, kappa: 0.3122, kb_relative_information_score: 0.1907, mean_absolute_error: 0.3347, mean_prior_absolute_error: 0.4202, weighted_recall: 0.748, number_of_instances: 1000, precision: 0.7314, predictive_accuracy: 0.748, prior_entropy: 0.8813, relative_absolute_error: 0.7966, root_mean_prior_squared_error: 0.4583, root_mean_squared_error: 0.4166, root_relative_squared_error: 0.9092, unweighted_recall: 0.6362,
sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer) (6)
Applies transformers to columns of an array or pandas DataFrame. This estimator allows different columns or column subsets of the input to be transformed separately and the features generated by each…
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sklearn.ensemble._forest.RandomForestClassifier (43)
A random forest classifier. A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive…
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sklearn.pipeline.Pipeline(Preprocessing=sklearn.compose._column_transformer.ColumnTransformer(categorical=sklearn.preprocessing._encoders.OneHotEncoder,continuous=sklearn.impute._base.SimpleImputer),Classifier=sklearn.ensemble._forest.RandomForestClassifier) (5)
Pipeline of transforms with a final estimator. Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be 'transforms', that is, they must implement…
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Taniya D 5 days ago
ran flow torch.nn.Model2.a524e7d6f2f5accd(1) on task Supervised Classification on data set IndoorScenes
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.4999, kappa: 0.0008, kb_relative_information_score: 0.0467, mean_absolute_error: 0.0294, mean_prior_absolute_error: 0.0292, weighted_recall: 0.0307, number_of_instances: 15620, predictive_accuracy: 0.0307, prior_entropy: 5.7539, relative_absolute_error: 1.0075, root_mean_prior_squared_error: 0.1208, root_mean_squared_error: 0.1212, root_relative_squared_error: 1.0039, unweighted_recall: 0.0155,
torch.nn.InvertedResidual.d22dd78f84c40331 (1)
Automatically created pytorch flow.
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torch.nn.Sequential.1feda6ba089730b (1)
Automatically created pytorch flow.
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torch.nn.Conv2dNormActivation.7d8d8dc642ed62dc (1)
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torch.nn.Conv2d.456a650dc0562c5e (1)
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torch.nn.Conv2d.d743748d114b4f7e (1)
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torch.nn.BatchNorm2d.61ba1f2cab2a09f4 (1)
Automatically created pytorch flow.
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torch.nn.ReLU6.3eb4665cc8da968f (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.d59817fc56f92fc4 (1)
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torch.nn.BatchNorm2d.191befbfe9063fcf (1)
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torch.nn.ReLU6.44fb1cc811031fc9 (1)
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torch.nn.InvertedResidual.e9523a34ff3f8168 (1)
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torch.nn.Sequential.ae567afc3109e376 (1)
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torch.nn.Sequential.dbfe2cf7f89b11ad (1)
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torch.nn.Conv2dNormActivation.3d8f7a9ec382ec2e (1)
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torch.nn.Conv2d.c1be8b49907a3e61 (1)
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torch.nn.Conv2dNormActivation.5802bd5ea4a59aff (1)
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torch.nn.Conv2d.bc38d1657157ffee (1)
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torch.nn.BatchNorm2d.350eee6951fd92eb (1)
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torch.nn.BatchNorm2d.f13c2b3649629e8c (1)
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torch.nn.ReLU6.574190a6ede0d7af (1)
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torch.nn.Conv2dNormActivation.3ff1d31fb345daba (1)
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torch.nn.ReLU6.a8a4e41b5565ea3f (1)
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torch.nn.Conv2d.6f99b87d9914ae79 (1)
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torch.nn.BatchNorm2d.3ca8176f25303155 (1)
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torch.nn.Conv2d.9fb1a0b175a6b99b (1)
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torch.nn.BatchNorm2d.b40b5e7a895cf910 (1)
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torch.nn.InvertedResidual.b3eb152be85d5575 (1)
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torch.nn.Sequential.9ee44b0b616b8f5 (1)
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torch.nn.ReLU6.4c3a1708a930a895 (1)
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torch.nn.Conv2dNormActivation.a886fb3b16da161 (1)
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torch.nn.Conv2d.bc110042fbb6a1fa (1)
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torch.nn.BatchNorm2d.48ce98e6cf2dfdb2 (1)
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torch.nn.InvertedResidual.87560cb09670e0df (1)
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torch.nn.Sequential.3dc46e015fabc6bf (1)
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torch.nn.Conv2dNormActivation.859c92051ff3efc4 (1)
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torch.nn.Conv2d.1e08e4b35f6e2295 (1)
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torch.nn.BatchNorm2d.9f812263897f2cdf (1)
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torch.nn.InvertedResidual.4f05d2363de136fb (1)
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torch.nn.Sequential.504b430bab24f666 (1)
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torch.nn.Conv2dNormActivation.53e44296e069eb9c (1)
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torch.nn.Conv2d.5bd85730405f2e60 (1)
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torch.nn.BatchNorm2d.5da6c4321903fbd3 (1)
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torch.nn.BatchNorm2d.1211121d1916a3d6 (1)
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torch.nn.ReLU6.9d3f7d1bc52a8930 (1)
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torch.nn.Conv2dNormActivation.b7f5ae9020afca80 (1)
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torch.nn.ReLU6.22e66080535a5381 (1)
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torch.nn.Conv2d.35f4812f780f75d3 (1)
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torch.nn.BatchNorm2d.99331f707e316ef9 (1)
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torch.nn.Conv2dNormActivation.e162b216f2453abd (1)
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torch.nn.Conv2d.dbaf726aa4fe9f46 (1)
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torch.nn.BatchNorm2d.88572129d04cd216 (1)
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torch.nn.Conv2d.c00f0fa89c45cac8 (1)
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torch.nn.BatchNorm2d.e379caa347d56f8e (1)
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torch.nn.ReLU6.910c67b612b80c2d (1)
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torch.nn.Conv2d.20fa48792ee69f07 (1)
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torch.nn.BatchNorm2d.a98e76a3156f7667 (1)
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torch.nn.ReLU6.3c6212731d542720 (1)
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torch.nn.Conv2d.d3e5149cb668f327 (1)
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torch.nn.BatchNorm2d.dbfa9986ba0e0386 (1)
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torch.nn.InvertedResidual.bd2ea58c1d8d8530 (1)
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torch.nn.Sequential.93a749eba9ebb40e (1)
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torch.nn.Conv2dNormActivation.84fea656f74347bb (1)
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torch.nn.InvertedResidual.490535bfb0a86714 (1)
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torch.nn.Sequential.13b2eaf67fa3be2a (1)
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torch.nn.Conv2dNormActivation.d12a7e8d4ce9e077 (1)
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torch.nn.Conv2d.c06e3e8ef4be9c4c (1)
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torch.nn.Conv2dNormActivation.dc7fddc0327b6aaf (1)
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torch.nn.Conv2d.dad6facea9970a7d (1)
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torch.nn.BatchNorm2d.f3369c71212c2ce8 (1)
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torch.nn.Conv2d.b5cb85426f5ac7a7 (1)
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torch.nn.BatchNorm2d.c4b10cb1c2c3a4c1 (1)
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torch.nn.ReLU6.61877bcef9ca3b35 (1)
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torch.nn.ReLU6.741b0e1b239779a (1)
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torch.nn.Conv2dNormActivation.3abff818eae04c86 (1)
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torch.nn.Conv2d.7eead612cda4d7e0 (1)
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torch.nn.ReLU6.d9d638aa144c25a6 (1)
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torch.nn.Conv2d.a1e0a65ad09e29 (1)
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torch.nn.BatchNorm2d.eae253ecc577e032 (1)
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torch.nn.BatchNorm2d.bb1a2bb1eccda0d9 (1)
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torch.nn.ReLU6.b31e01e44c9c6459 (1)
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torch.nn.Conv2d.24935440d761f8f6 (1)
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torch.nn.BatchNorm2d.fc48492d1d1ab61a (1)
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torch.nn.ReLU6.5c50c855400fc4b9 (1)
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torch.nn.Conv2dNormActivation.89ec085886de20cc (1)
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torch.nn.BatchNorm2d.cb55104a942370c9 (1)
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torch.nn.ReLU6.37c9cc82a3ab22fd (1)
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torch.nn.Conv2d.6169b31edfb7ab41 (1)
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torch.nn.BatchNorm2d.bce8c54d8442d1df (1)
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torch.nn.Conv2d.e4959ee8df08451e (1)
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torch.nn.BatchNorm2d.be37d6d2ce4e8f9c (1)
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torch.nn.ReLU6.624cc0168c93deb5 (1)
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torch.nn.Conv2dNormActivation.1e45572db3723ec (1)
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torch.nn.Conv2d.f39d0f8aff6cddc0 (1)
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torch.nn.BatchNorm2d.873890341209d184 (1)
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torch.nn.ReLU6.cdfce78a1b6145 (1)
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