Welcome to Xcessiv’s documentation!

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

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Features

• 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

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Define your base learners and performance metrics

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Keep track of hundreds of different model-hyperparameter combinations

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Effortlessly choose your base learners and create stacked ensembles

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Contents

• Installation and Configuration
  • Installing and running Redis
  • Installing Xcessiv
  • Configuration
• Walkthrough of Typical Xcessiv Workflow
  • Starting Xcessiv
  • Creating/Opening a Project
  • Importing your dataset into Xcessiv
  • Defining your base learners and metrics
  • Optimizing your base learners’ hyperparameters
  • Creating a stacked ensemble
  • Exporting your stacked ensemble
• Automated Runs
  • TPOT base learner construction
  • Bayesian Hyperparameter Search
  • Greedy Forward Model Selection
• Xcessiv and Third Party Libraries
  • Xcessiv with TPOT

Indices and tables

• Index
• Module Index
• Search Page