Photonai Is A High Code Framework for Scikit-Learn, Keras, Tensortorch, and other Frameworks
Photonai has different end-user objectives than Scikit-Learn. I recommend both for Machine Learning. Photonai is a high-code framework that works well for Kaggle competitions.
Brief Comparison Scikit-Learn and?Photonai
Use Scikit-Learn as a core Machine Learning (ML) library and use other packages to broaden your ML solutions. As an ML scientist, you may use Deep Learning (DL) frameworks such as Pytorch or Tensorflow for specialized ML models.
Photoai incorporates Scikit-Learn and any other ML/DL frameworks with one unifying paradigm. Photonai adopts Scikit-Learn’s?Estimatorand?Transformerclasses.
Photonai is a great Kaggle framework or framework for your enterprise solutions (work).
Scikit-Learn supplies the majority of Photonai’s core of machine learning (ML) algorithms.
Photonai adds code that reduces manual coding and error by transforming pre- and post-learner algorithms into components with parameters. Examples of components are several data cleaners, scalers, imputers, class balancers, cross-validators, hyper-parameter tuners, and ensembles.