OpenML

An implementation of the evaluation measure "EuclideanDistance"

An implementation of the evaluation measure "PolynomialKernel"

An implementation of the evaluation measure "RBFKernel"

An implementation of the evaluation measure "area_under_roc_curve"

An implementation of the evaluation measure "average_cost"

An implementation of the evaluation measure "build_cpu_time"

An implementation of the evaluation measure "build_memory"

An implementation of the evaluation measure "class_complexity"

An implementation of the evaluation measure "class_complexity_gain"

An implementation of the evaluation measure "confusion_matrix"

An implementation of the evaluation measure "correlation_coefficient"

An implementation of the evaluation measure "f_measure"

An implementation of the evaluation measure "kappa"

An implementation of the evaluation measure "kb_relative_information_score"

An implementation of the evaluation measure "kohavi_wolpert_bias_squared"

An implementation of the evaluation measure "kohavi_wolpert_error"

An implementation of the evaluation measure "kohavi_wolpert_sigma_squared"

An implementation of the evaluation measure "kohavi_wolpert_variance"

An implementation of the evaluation measure "kononenko_bratko_information_score"

An implementation of the evaluation measure "matthews_correlation_coefficient"

An implementation of the evaluation measure "mean_absolute_error"

An implementation of the evaluation measure "mean_class_complexity"

An implementation of the evaluation measure "mean_class_complexity_gain"

An implementation of the evaluation measure "mean_f_measure"

An implementation of the evaluation measure "mean_kononenko_bratko_information_score"

An implementation of the evaluation measure "mean_precision"

An implementation of the evaluation measure "mean_prior_absolute_error"

An implementation of the evaluation measure "mean_prior_class_complexity"

An implementation of the evaluation measure "mean_recall"

An implementation of the evaluation measure "mean_weighted_area_under_roc_curve"

An implementation of the evaluation measure "mean_weighted_f_measure"

An implementation of the evaluation measure "mean_weighted_precision"

An implementation of the evaluation measure "mean_weighted_recall"

An implementation of the evaluation measure "number_of_instances"

An implementation of the evaluation measure "precision"

An implementation of the evaluation measure "predictive_accuracy"

An implementation of the evaluation measure "prior_class_complexity"

An implementation of the evaluation measure "prior_entropy"

An implementation of the evaluation measure "recall"

An implementation of the evaluation measure "relative_absolute_error"

An implementation of the evaluation measure "root_mean_prior_squared_error"

An implementation of the evaluation measure "root_mean_squared_error"

An implementation of the evaluation measure "root_relative_squared_error"

An implementation of the evaluation measure "run_cpu_time"

An implementation of the evaluation measure "run_memory"

An implementation of the evaluation measure "run_virtual_memory"

An implementation of the evaluation measure "single_point_area_under_roc_curve"

An implementation of the evaluation measure "total_cost"

An implementation of the evaluation measure "unclassified_instance_count"

An implementation of the evaluation measure "webb_bias"

An implementation of the evaluation measure "webb_error"

An implementation of the evaluation measure "webb_variance"

Default information about OS, JVM, installations, etc.

Every GB of RAM deployed for 1 hour equals one RAM-Hour.

Information of the CPU performance on which the run was performed

R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.

George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.

William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.

Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.

Weka implementation of DecisionStump

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.

Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.

D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.

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…

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,…

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,…

le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.

Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.

Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.

J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.

A Hoeffding tree (VFDT) is an incremental, anytime decision tree induction algorithm that is capable of learning from massive data streams, assuming that the distribution generating examples does not…

Moa implementation of LeveragingBag

Moa implementation of LeveragingBag

Moa implementation of OzaBag

Moa implementation of OzaBagAdwin

Moa implementation of OzaBoost

Moa implementation of OzaBoostAdwin

Moa implementation of RandomRules

Moa implementation of RuleClassifier

Moa implementation of ASHoeffdingTree

Moa implementation of HoeffdingAdaptiveTree

Moa implementation of RandomHoeffdingTree

Moa implementation of MajorityClass

Moa implementation of WEKAClassifier

Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.