Welcome to Xcessiv’s documentation!

Xcessiv is a web-based application for quick and scalable hyperparameter tuning and stacked ensembling in Python.


  • 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
  • Direct integration with TPOT for automated pipeline construction
  • Automated hyperparameter search through Bayesian optimization
  • 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
  • Automated ensemble construction through greedy forward model selection
  • Export your stacked ensemble as a standalone Python file to support multiple levels of stacking

Define your base learners and performance metrics

Base learner gif

Keep track of hundreds of different model-hyperparameter combinations

List base learner gif

Effortlessly choose your base learners and create stacked ensembles

Ensemble gif

Indices and tables