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Fully managed web application for automated machine learning

What is Xcessiv?

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
Xcessiv is a tool in the Machine Learning Tools category of a tech stack.
Xcessiv is an open source tool with 1.2K GitHub stars and 93 GitHub forks. Here’s a link to Xcessiv's open source repository on GitHub

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Xcessiv's 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

Xcessiv Alternatives & Comparisons

What are some alternatives to Xcessiv?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
ML Kit
ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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