Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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Automatically created pytorch flow.
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0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9967, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 0.92, mean_absolute_error: 0.0423, mean_prior_absolute_error: 0.4444, weighted_recall: 0.9533, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, relative_absolute_error: 0.0952, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1479, root_relative_squared_error: 0.3138, unweighted_recall: 0.9533,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9966, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 0.9208, mean_absolute_error: 0.0422, mean_prior_absolute_error: 0.4444, weighted_recall: 0.9533, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, relative_absolute_error: 0.095, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1445, root_relative_squared_error: 0.3064, unweighted_recall: 0.9533,
0 likes - 0 downloads - 0 reach - area_under_roc_curve: 0.9962, f_measure: 0.9533, kappa: 0.93, kb_relative_information_score: 0.9194, mean_absolute_error: 0.0429, mean_prior_absolute_error: 0.4444, weighted_recall: 0.9533, number_of_instances: 150, precision: 0.9534, predictive_accuracy: 0.9533, prior_entropy: 1.585, relative_absolute_error: 0.0965, root_mean_prior_squared_error: 0.4714, root_mean_squared_error: 0.1488, root_relative_squared_error: 0.3157, unweighted_recall: 0.9533,
An extra-trees classifier. This class implements a meta estimator that fits a number of randomized decision trees (a.k.a. extra-trees) on various sub-samples of the dataset and uses averaging to…
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3 datasets, 3 tasks, 0 flows, 0 runs
Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…
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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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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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Standardize features by removing the mean and scaling to unit variance The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if…
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DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.
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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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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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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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Imputation transformer for completing missing values.
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Standardize features by removing the mean and scaling to unit variance The standard score of a sample `x` is calculated as: z = (x - u) / s where `u` is the mean of the training samples or zero if…
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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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Duplicate class alias for sklearn's SimpleImputer Helps bypass the sklearn extension duplicate operation check
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