Stacked ensembles are simple in theory. You combine the predictions of smaller models and feed those into another model. However, in practice, implementing them can be a major headache.

Xcessiv holds your hand through all the implementation details of creating and optimizing stacked ensembles so you're free to fully define only the things you care about.

• Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code
• Any model following the Scikit-learn API can be used as a base learner
• Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches
• Easy management and comparison of hundreds of different model-hyperparameter combinations
• Automatic saving of generated secondary meta-features
• Stacked ensemble creation in a few clicks

You can find installation instructions and detailed documentation hosted here

Xcessiv fits in the model building part of the process after data preparation and feature engineering. At this point, there is no universally acknowledged way of determining which algorithm will work best for a particular dataset (see No Free Lunch Theorem), and while heuristic optimization methods do exist, things often break down into trial and error as you try to find the best model-hyperparameter combinations.

Stacking is an almost surefire method to improve performance beyond that of any single model, however, the complexity of proper implementation often makes it impractical to apply them in practice outside of Kaggle competitions. Xcessiv aims to make the construction of stacked ensembles as painless as possible and lower the barrier for entry.

Absolutely! Even without the ensembling functionality, the sheer amount of utility provided by keeping track of the performance of hundreds, and even thousands of ML models and hyperparameter combinations is a huge boon.

You can choose whether to generate meta-features through cross-validation (stacked generalization) or with a holdout set (blending). You can read about these two methods and a lot more about stacked ensembles in the Kaggle Ensembling Guide. It's a great article and provides most of the inspiration for this project.

Xcessiv is in its very early stages and needs the open-source community to guide it along.

There are many ways to contribute to Xcessiv. You could report a bug, suggest a feature, submit a pull request, improve documentation, and many more.

If you would like to contribute something, please visit our Contributor Guidelines.

Xcessiv is currently in alpha and is unstable. Future versions are not guaranteed to be backwards-compatible with current project files.