Fedot is the AutoML-like framework for the automated generation of the data-driven composite models. It can solve classification, regression, clustering, and forecasting problems.
In practice, the existing AutoML solutions are really effective for the limited set of problems only. During the model learning, modern AutoML mostly focused on relatively simple tasks of hyperparameters optimization, input data preprocessing, selecting a single model or a set of models (this approach is also referred to as the Combined Algorithm Selection and Hyperparameters optimization - CASH) since the overall learning and meta-learning process is extremely expensive. In the Fedot we have used the composite models concept. We claim, that it allows us to solve many actual real-world problems in a more efficient way. Also, we are aimed to outperform the existing solutions even for well-known benchmarks (e.g. PMLB datasets).
We provide a constant extension of Fedot’s feature set. However, any Pull Requests and issues from external contributors that introduce or suggests the new features will be appreciated. You can create your pull request or issue in the main repository of Fedot.
Yes, you can. The Fedot is published under the BSD-3 license. Also, we will be happy to help the users to adopt Fedot to their needs.
We decided to use this archaic Russian first name to add a bit of fantasy spirit into the development process.