OpenML
A decision tree classifier.
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A decision tree classifier.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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A decision tree classifier.
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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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Clone of SimpleImputer to circumvent OpenML restriction
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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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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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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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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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A decision tree classifier.
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
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Weka implementation.
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le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
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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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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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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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Automatically created scikit-learn flow.
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A decision tree classifier.
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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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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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Automatically created scikit-learn flow.
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