OpenML

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.

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.

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…

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.

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.

Imputation transformer for completing missing values.

A decision tree classifier.

Imputation transformer for completing missing values.

A decision tree classifier.

A decision tree classifier.

Imputation transformer for completing missing values.

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…

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…

Imputation transformer for completing missing values.

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…

Duplicate class alias for sklearn's SimpleImputer Helps bypass the sklearn extension duplicate operation check

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.…

A decision tree classifier.

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…

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…

Imputation transformer for completing missing values.

Duplicate class alias for sklearn's SimpleImputer Helps bypass the sklearn extension duplicate operation check

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.…

A decision tree classifier.

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…

Imputation transformer for completing missing values.

Duplicate class alias for sklearn's SimpleImputer Helps bypass the sklearn extension duplicate operation check

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.

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.…

A decision tree classifier.

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…

A decision tree classifier.

Imputation transformer for completing missing values.