Oleksandr's flows

Learner mlr.classif.knn from package(s) class.

Learner mlr.classif.knn from package(s) class.

Learner mlr.classif.rpart from package(s) rpart.

Learner mlr.classif.randomForest from package(s) randomForest.

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…

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…

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…

Classifier implementing the k-nearest neighbors vote.

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.

A decision tree classifier.