OpenML

A decision tree classifier.

Imputation transformer for completing missing values.

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.

Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…

A decision tree classifier.

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…

DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.

A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…

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…

Imputation transformer for completing missing values.

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.

A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…

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…

A Bagging classifier. A Bagging classifier is an ensemble meta-estimator that fits base classifiers each on random subsets of the original dataset and then aggregate their individual predictions…

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…

Imputation transformer for completing missing values.

Imputation transformer for completing missing values.

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.

Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…

A decision tree classifier.

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…

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…

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…

Imputation transformer for completing missing values.

Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "score_samples", "predict", "predict_proba", "decision_function", "transform" and…

A decision tree classifier.

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…

DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.

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…

DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems.

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

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.

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.

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…

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

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

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.

Weka implementation of MultilayerPerceptron

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

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

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

Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…

Encode categorical features as an integer array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The…

Encode categorical features as an integer array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The…

Encode categorical features as an integer array. The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The…

test description

Soft Voting/Majority Rule classifier for unfitted estimators. .. versionadded:: 0.17

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…

Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict",…

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…

Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a "fit" and a "score" method. It also implements "predict",…

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…

Randomized search on hyper parameters. RandomizedSearchCV implements a "fit" and a "score" method. It also implements "predict", "predict_proba", "decision_function", "transform" and…

Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…

Gaussian Naive Bayes (GaussianNB) Can perform online updates to model parameters via :meth:`partial_fit`. For details on algorithm used to update feature means and variance online, see Stanford CS…

A decision tree classifier.

A decision tree classifier.

A decision tree classifier.

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…

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.…