OpenML
Automatically created keras flow.
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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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Implementation of the scikit-learn API for XGBoost classification.
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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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C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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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 Centering and scaling happen independently on each feature by computing the relevant statistics on the samples in the training…
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C-Support Vector Classification. The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than…
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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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A decision tree classifier.
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This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark Precise benchmark version information could not be determined. constantpredictor version: stable
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C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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Automatically created keras flow.
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Automatically created keras flow.
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Automatically created keras flow.
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Automatically created keras flow.
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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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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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C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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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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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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Imputation transformer for completing missing values.
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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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A decision tree classifier.
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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…
0 runs0 likes0 downloads0 reach0 impact
C-Support Vector Classification. The implementation is based on libsvm. The fit time scales at least quadratically with the number of samples and may be impractical beyond tens of thousands of…
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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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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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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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A decision tree classifier.
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Weka implementation of AutoWEKAClassifier
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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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A random subgroups classifier. A random subgroups classifier is a meta estimator that fits a number subgroups on various sub-samples of the dataset. The sub-sample size is controlled with the…
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Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…
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A decision tree classifier.
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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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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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Classifier implementing the k-nearest neighbors vote.
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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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Learner mlr.classif.rpart.tuned from package(s) rpart.
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Learner mlr.classif.rpart.imputed.filtered from package(s) rpart.
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Learner mlr.classif.rpart from package(s) rpart.
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Gradient Boosting for classification. GB builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage…
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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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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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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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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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A decision tree classifier.
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Imputation transformer for completing missing values.
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Automatically created keras flow.
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Automatically created tensorflow 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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