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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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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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…
1 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
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…
1 runs0 likes0 downloads0 reach0 impact
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…
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Classifier implementing the k-nearest neighbors vote.
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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…
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Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification. URL http://www.csie.ntu.edu.tw/~cjlin/liblinear/.
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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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Weka implementation of RBFClassifier
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D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
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Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
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Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.
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Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
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Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Machine Learning. 95(1-2):161-205. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European…
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Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.
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Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
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Weka implementation of ConjunctiveRule
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Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.
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William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.
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Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of…
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Weka implementation of DecisionStump
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Joao Gama (2004). Functional Trees. Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees.
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R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
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Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
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Arie Ben-David (1992). Automatic Generation of Symbolic Multiattribute Ordinal Knowledge-Based DSSs: methodology and Applications. Decision Sciences. 23:1357-1372.
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Brent Martin (1995). Instance-Based learning: Nearest Neighbor With Generalization. Hamilton, New Zealand. Sylvain Roy (2002). Nearest Neighbor With Generalization. Christchurch, New Zealand.
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Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.
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Weka implementation of REPTree
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Weka implementation of RandomTree
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Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
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An implementation of the evaluation measure "EuclideanDistance"
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An implementation of the evaluation measure "PolynomialKernel"
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An implementation of the evaluation measure "RBFKernel"
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An implementation of the evaluation measure "average_cost"
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An implementation of the evaluation measure "build_cpu_time"
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An implementation of the evaluation measure "build_memory"
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An implementation of the evaluation measure "class_complexity"
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An implementation of the evaluation measure "class_complexity_gain"
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An implementation of the evaluation measure "confusion_matrix"
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An implementation of the evaluation measure "correlation_coefficient"
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An implementation of the evaluation measure "f_measure"
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An implementation of the evaluation measure "kappa"
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An implementation of the evaluation measure "kb_relative_information_score"
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An implementation of the evaluation measure "kohavi_wolpert_bias_squared"
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An implementation of the evaluation measure "kohavi_wolpert_error"
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An implementation of the evaluation measure "kohavi_wolpert_sigma_squared"
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An implementation of the evaluation measure "kohavi_wolpert_variance"
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An implementation of the evaluation measure "kononenko_bratko_information_score"
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An implementation of the evaluation measure "matthews_correlation_coefficient"
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An implementation of the evaluation measure "mean_absolute_error"
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An implementation of the evaluation measure "mean_class_complexity"
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An implementation of the evaluation measure "mean_class_complexity_gain"
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An implementation of the evaluation measure "mean_f_measure"
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An implementation of the evaluation measure "mean_kononenko_bratko_information_score"
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An implementation of the evaluation measure "mean_precision"
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An implementation of the evaluation measure "mean_prior_absolute_error"
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An implementation of the evaluation measure "mean_prior_class_complexity"
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An implementation of the evaluation measure "mean_recall"
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An implementation of the evaluation measure "mean_weighted_area_under_roc_curve"
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An implementation of the evaluation measure "mean_weighted_f_measure"
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An implementation of the evaluation measure "mean_weighted_precision"
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An implementation of the evaluation measure "mean_weighted_recall"
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An implementation of the evaluation measure "number_of_instances"
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An implementation of the evaluation measure "precision"
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An implementation of the evaluation measure "predictive_accuracy"
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An implementation of the evaluation measure "prior_class_complexity"
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An implementation of the evaluation measure "prior_entropy"
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An implementation of the evaluation measure "recall"
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An implementation of the evaluation measure "relative_absolute_error"
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An implementation of the evaluation measure "root_mean_prior_squared_error"
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An implementation of the evaluation measure "root_mean_squared_error"
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An implementation of the evaluation measure "root_relative_squared_error"
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An implementation of the evaluation measure "run_cpu_time"
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An implementation of the evaluation measure "run_memory"
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An implementation of the evaluation measure "run_virtual_memory"
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An implementation of the evaluation measure "single_point_area_under_roc_curve"
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An implementation of the evaluation measure "total_cost"
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An implementation of the evaluation measure "unclassified_instance_count"
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An implementation of the evaluation measure "webb_bias"
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An implementation of the evaluation measure "webb_error"
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An implementation of the evaluation measure "webb_variance"
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Default information about OS, JVM, installations, etc.
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Every GB of RAM deployed for 1 hour equals one RAM-Hour.
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Information of the CPU performance on which the run was performed
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G.F. Cooper, E. Herskovits (1990). A Bayesian method for constructing Bayesian belief networks from databases. G. Cooper, E. Herskovits (1992). A Bayesian method for the induction of probabilistic…
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Weka implementation of SGD
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Weka implementation of PolyKernel
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Weka implementation of RBFKernel
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Weka implementation of REPTree
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R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
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Weka implementation of LinearRegression
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