Welcome to scikit-ext’s documentation!

# scikit-ext : various scikit-learn extensions

### About The scikit_ext package contains various scikit-learn extensions, built entirely on top of sklearn base classes. The package is separated into two modules, estimators and scorers.

### Estimators - IterRandomEstimator: Meta-Estimator intended primarily for unsupervised

estimators whose fitted model can be heavily dependent on an arbitrary random initialization state. It is best used for problems where a fit_predict method is intended, so the only data used for prediction will be the same data on which the model was fitted.
  • OptimizedEnsemble: An optimized ensemble class. Will find the optimal n_estimators
    parameter for the given ensemble estimator, according to the specified input parameters.
  • OneVsRestAdjClassifier: One-Vs-Rest multiclass strategy. The adjusted version is a custom
    extension which overwrites the inherited predict_proba method with a more flexible method allowing custom normalization for the predicted probabilities. Any norm argument that can be passed directly to sklearn.preprocessing.normalize is allowed. Additionally, norm=None will skip the normalization step alltogeter. To mimick the inherited OneVsRestClassfier behavior, set norm=’l2’. All other methods are inherited from OneVsRestClassifier.

### Scorers - _TimeScorer: Score using estimated prediction latency of estimator. - _MemoryScorer: Score using estimated memory of pickled estimator object. - _CombinedScorer: Score combining multiple scorers by averaging their scores. - cluster_distribution_score: Scoring function which scores the resulting cluster distribution accross classes.

A more even distribution indicates a higher score.

### Authors

Evan Harris

### License

This project is licensed under the MIT License - see the LICENSE file for details

Indices and tables