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Pieter Gijsbers
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Pieter's flows

A decision tree classifier.
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This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: d5c73433ffc6c57c88113a897213a6bc057e5846 RandomForest version: 1.2.2
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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…
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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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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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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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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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This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: 75567510ce887b7b8aa857b9a1f9f29d1775813c constantpredictor version: stable
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This flow is generated by the automl benchmark: https://github.com/openml/automlbenchmark.git Repository commit: f0086d1bd6488395413bfe1f6caf8f9a34b8910d constantpredictor version: stable
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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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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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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 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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Apply a power transform featurewise to make data more Gaussian-like. Power transforms are a family of parametric, monotonic transformations that are applied to make data more Gaussian-like. This is…
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Scale features using statistics that are robust to outliers. This Scaler removes the median and scales the data according to the quantile range (defaults to IQR: Interquartile Range). The IQR is the…
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Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero…
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A decision tree classifier.
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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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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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A decision tree classifier.
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Automatically created scikit-learn flow.
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Tuned Random Forest baseline as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
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Random Forest baseline as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
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Automatically created scikit-learn flow.
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Auto-WEKA 2.6 as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
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H2O AutoML 3.24.0.1 as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
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TPOT as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
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Auto-sklearn as set up by the AutoML BenchmarkSource: source: https://github.com/openml/automlbenchmark/releases/tag/v0.9
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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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Flow generated by run_task
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Flow generated by run_task
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