Matthias's flows

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…

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.

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.

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.

A decision tree classifier.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.

Automatically created scikit-learn flow.