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TESTb0e3ae52cbsklearn.dummy.DummyClassifier (1)
test description
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TEST472e94bcd7sklearn.dummy.DummyClassifier (1)
test description
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TEST4d384bec8c-pandas_testing_dataset (3)
Synthetic dataset created from a Pandas DataFrame with Sparse columns
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5 instances - 1 features - 5 classes - 0 missing values
TESTabe563233asklearn.tree._classes.DecisionTreeClassifier (1)
A decision tree classifier.
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TEST753cafd9b3sklearn.preprocessing._encoders.OneHotEncoder (1)
Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features.…
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TEST753cafd9b3sklearn.feature_selection._variance_threshold.VarianceThreshold (1)
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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TEST753cafd9b3sklearn.tree._classes.DecisionTreeClassifier (1)
A decision tree classifier.
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TEST753cafd9b3sklearn.pipeline.Pipeline(imputation=sklearn.impute._base.SimpleImputer,hotencoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.tree._classes.DecisionTreeClassifier) (1)
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 fit…
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TEST45ac2ae508sklearn.preprocessing._encoders.OrdinalEncoder (1)
Encode categorical features as an integer array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The…
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TEST753cafd9b3sklearn.impute._base.SimpleImputer (1)
Imputation transformer for completing missing values.
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Test Test 2 months ago
ran flow sklearn.ensemble._forest.RandomForestClassifier(7) on task Supervised Classification on data set diabetes
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.8264, f_measure: 0.7531, kappa: 0.463, kb_relative_information_score: 0.3136, mean_absolute_error: 0.3196, mean_prior_absolute_error: 0.4589, weighted_recall: 0.7549, number_of_instances: 253, precision: 0.752, predictive_accuracy: 0.7549, prior_entropy: 0.9463, relative_absolute_error: 0.6964, root_mean_prior_squared_error: 0.4813, root_mean_squared_error: 0.4019, root_relative_squared_error: 0.8351, unweighted_recall: 0.7283,
TEST4d384bec8c-pandas_testing_dataset (2)
Synthetic dataset created from a Pandas DataFrame with Sparse columns
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4 instances - 3 features - 0 classes - 0 missing values
TEST2f42e87608sklearn.preprocessing._encoders.OneHotEncoder (1)
Encode categorical features as a one-hot numeric array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features.…
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TEST2f42e87608sklearn.feature_selection._variance_threshold.VarianceThreshold (1)
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.
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TEST2f42e87608sklearn.tree._classes.DecisionTreeClassifier (1)
A decision tree classifier.
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TEST2f42e87608sklearn.pipeline.Pipeline(imputation=sklearn.impute._base.SimpleImputer,hotencoding=sklearn.preprocessing._encoders.OneHotEncoder,variencethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,classifier=sklearn.tree._classes.DecisionTreeClassifier) (1)
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 fit…
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TEST2f42e87608sklearn.impute._base.SimpleImputer (1)
Imputation transformer for completing missing values.
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TESTfb2ad51030sklearn.linear_model._logistic.LogisticRegression (1)
Logistic Regression (aka logit, MaxEnt) classifier. In the multiclass case, the training algorithm uses the one-vs-rest (OvR) scheme if the 'multi_class' option is set to 'ovr', and uses the…
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TESTfb2ad51030sklearn.ensemble._voting.VotingClassifier(lr=sklearn.linear_model._logistic.LogisticRegression) (1)
Soft Voting/Majority Rule classifier for unfitted estimators.
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TEST90da58d76csklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
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TEST4d384bec8c-pandas_testing_dataset (1)
Synthetic dataset created from a Pandas DataFrame
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5 instances - 6 features - 2 classes - 0 missing values
TESTf553e4d7dbsklearn.naive_bayes.GaussianNB (1)
Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…
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TESTe4367487a3-NumPy_testing_dataset (1)
Synthetic dataset created from a NumPy array
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3 instances - 4 features - 0 classes - 0 missing values
TEST5d9095e1cd-ModifiedWeather (1)
Testing dataset upload when the data is a list of lists
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14 instances - 6 features - 2 classes - 0 missing values
IndoorScenes (1)
Test
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15620 instances - 3 features - 67 classes - 0 missing values
torch.nn.Conv2d.93e01ac9e48c7871 (1)
Automatically created pytorch flow.
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torch.nn.Conv2d.12ab2f43e6421d9e (1)
Automatically created pytorch flow.
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torch.nn.Linear.b841706751098e29 (1)
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torch.nn.Linear.56512115bc57af09 (1)
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torch.nn.Linear.48c0950f2c64ffa8 (1)
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torch.nn.MaxPool2d.ef55d36efd57d4ed (1)
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torch.nn.Net.117c1d8d46467e91 (1)
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torch.nn.BatchNorm2d.ac5b527afb562c5b (1)
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torch.nn.BatchNorm2d.ab9e520ced718ccb (1)
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torch.nn.Conv2d.27c7df07e167cc6c (1)
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torch.nn.Conv2d.eed954439364ed (1)
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torch.nn.Conv2d.ef48e2b86e77e3c0 (1)
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torch.nn.ReLU.6e87958815a42129 (1)
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torch.nn.Sequential.437ea08f6e721e78 (1)
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torch.nn.Bottleneck.1e379120f4581e6f (1)
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torch.nn.BatchNorm2d.43f9cdcf896adeda (1)
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torch.nn.BatchNorm2d.62a47860c092cd8f (1)
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torch.nn.BatchNorm2d.b5c53ac16accfaae (1)
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torch.nn.Conv2d.f4f8a605c44e88f (1)
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torch.nn.Conv2d.9fe4f4125830d5a0 (1)
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torch.nn.Conv2d.30513c21a3c29d2f (1)
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torch.nn.Sequential.aca70b9e5966700a (1)
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torch.nn.Conv2d.387dcdae7e5f0c62 (1)
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torch.nn.BatchNorm2d.4b944a61b51a0f7 (1)
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torch.nn.ReLU.3c189c0791adabd9 (1)
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torch.nn.Bottleneck.4d385d59a6696b4d (1)
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torch.nn.BatchNorm2d.b9b47f3c8bc80210 (1)
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torch.nn.BatchNorm2d.9d3a73e18fec6951 (1)
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torch.nn.BatchNorm2d.a46f55c09eebbc6e (1)
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torch.nn.Conv2d.a7568fef84e5a32 (1)
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torch.nn.Conv2d.f676d6b94fea7274 (1)
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torch.nn.Conv2d.196cbb638e8f2f05 (1)
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torch.nn.ReLU.4e7689bbd729f4cd (1)
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torch.nn.BatchNorm2d.1131fc9c18167246 (1)
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torch.nn.ReLU.778ba0f99b68e75b (1)
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torch.nn.Bottleneck.bf1a758f7b3e9f6f (1)
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torch.nn.BatchNorm2d.5f35e870fbccb917 (1)
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torch.nn.BatchNorm2d.a35cd068b2c90fca (1)
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torch.nn.BatchNorm2d.7ac13416eff123bc (1)
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torch.nn.Conv2d.28eae7e61b194549 (1)
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torch.nn.Conv2d.4425e7545e5c570 (1)
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torch.nn.Conv2d.d42140fdcf6cc083 (1)
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torch.nn.ReLU.3288cb717076fdd3 (1)
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torch.nn.Bottleneck.335581bc72e134f9 (1)
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torch.nn.BatchNorm2d.f5588ce632a5cbe5 (1)
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torch.nn.BatchNorm2d.8bf8137dc04ff5f1 (1)
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torch.nn.BatchNorm2d.55076367f18fb284 (1)
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torch.nn.Conv2d.59029d71109e5a4b (1)
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torch.nn.Conv2d.d0314d61937b776e (1)
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torch.nn.Conv2d.fe46122d88f8e6 (1)
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torch.nn.ReLU.5ae90460d98d1988 (1)
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torch.nn.Sequential.b1d7c96e4e99fa (1)
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torch.nn.Bottleneck.b27a753e1cd26312 (1)
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torch.nn.BatchNorm2d.247dc66acb3689fd (1)
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torch.nn.BatchNorm2d.eca084ed7610d5d9 (1)
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torch.nn.BatchNorm2d.eccccc57cc71dc1c (1)
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torch.nn.Conv2d.2f1e46c0c53e0b02 (1)
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torch.nn.Conv2d.b4f9cefea3944688 (1)
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torch.nn.Conv2d.33eb2841f46f4be7 (1)
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torch.nn.Sequential.4dbee3d45edd0e27 (1)
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torch.nn.Conv2d.12b608e63c2a4a11 (1)
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torch.nn.BatchNorm2d.fa0e938f74d7644b (1)
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torch.nn.ReLU.225811f617a4e521 (1)
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torch.nn.Bottleneck.5924bbf1722f768b (1)
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torch.nn.BatchNorm2d.bc2f26f3ca5648b (1)
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torch.nn.BatchNorm2d.26fc222d3ed53931 (1)
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torch.nn.BatchNorm2d.4b3c29b922a3603f (1)
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torch.nn.Conv2d.2b00f14c4eb7469 (1)
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torch.nn.Conv2d.7bb062bcc2dd5d59 (1)
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torch.nn.Conv2d.9cd159194adc4a1d (1)
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torch.nn.ReLU.c8b6156b216238bd (1)
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torch.nn.Bottleneck.2cc7ce791044c6a2 (1)
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torch.nn.BatchNorm2d.ab59be34529ceb75 (1)
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torch.nn.BatchNorm2d.3efbcd305eeda38e (1)
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torch.nn.BatchNorm2d.f04eb93cec0222f8 (1)
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