This is the class and function reference of the package.
This module provides ensemble estimators which work transformed output-space.
| ensemble.ExtraTreesClassifier([...]) | An extra-trees classifier. |
| ensemble.ExtraTreesRegressor([n_estimators, ...]) | An extra-trees regressor. |
| ensemble.LazyBaggingClassifier([...]) | A lazy bagging classifier. |
| ensemble.LazyBaggingRegressor([...]) | A lazy bagging regressor. |
| ensemble.RandomForestClassifier([...]) | A random forest classifier. |
| ensemble.RandomForestRegressor([...]) | A random forest regressor. |
Module for datasets loading and fetchers.
| datasets.fetch_drug_interaction([data_home]) | Fetch the drug-interaction dataset |
| datasets.fetch_protein_interaction([data_home]) | Fetch the protein-interaction dataset |
This module provides dimensionality reduction methods based on random projection.
| random_projection.RademacherRandomProjection([...]) | Rademacher random projection |
| random_projection.AchlioptasRandomProjection([...]) | Sparse random projection using Achlioptas random matrix |
| random_projection.SampledHadamardProjection([...]) | Subsample Hadamard random projection |
| random_projection.SampledIdentityProjection([...]) | Subsample identity matrix projection |
This module provides general purpose meta-transformer.
| transformer.FixedStateTransformer(transformer) | Fixe the random_state of the transformer |
This module gathers tree-based methods, including decision, regression and randomized trees. Single and multi-output problems are both handled.
This module also provides tree-based estimators which worked transform output-space.
| tree.DecisionTreeClassifier([criterion, ...]) | A decision tree classifier. |
| tree.DecisionTreeRegressor([criterion, ...]) | A decision tree regressor. |