Aerosolve vs Xcessiv: What are the differences?
Developers describe Aerosolve as "A machine learning package built for humans (created by Airbnb)". This library is meant to be used with sparse, interpretable features such as those that commonly occur in search (search keywords, filters) or pricing (number of rooms, location, price). It is not as interpretable with problems with very dense non-human interpretable features such as raw pixels or audio samples. On the other hand, Xcessiv is detailed as "Fully managed web application for automated machine learning". A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.
Aerosolve and Xcessiv can be categorized as "Machine Learning" tools.
Some of the features offered by Aerosolve are:
- A thrift based feature representation that enables pairwise ranking loss and single context multiple item representation.
- A feature transform language gives the user a lot of control over the features
- Human friendly debuggable models
On the other hand, Xcessiv provides the following key 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
Aerosolve and Xcessiv are both open source tools. It seems that Aerosolve with 4.58K GitHub stars and 578 forks on GitHub has more adoption than Xcessiv with 1.19K GitHub stars and 95 GitHub forks.