PyCaret + SKORCH: Build PyTorch Neural Networks using Minimal Code
A low-code guide to build PyTorch Neural Networks with Pycaret
Almost in every machine learning project, we train and evaluate multiple machine learning models. This often involves writing multiple lines of imports, many function calls, print statements to train individual models and compare the results across the models. The code becomes a mess when comparing different models with cross-validation loops or ensembling the models. Over time, it gets even messier when we move from classification models to regression models or vice-versa. We end up copying snippets of code from one place to another, creating chaos! We can easily avoid this chaos by just importing PyCaret!
PyCaret is a low-code machine library that allows you to create, train, and test ML models via a unified API given a regression or classification problem. PyCaret also offers various steps in a machine learning project, from data preparation to model deployment with a minimal amount of code. It can work with any model/library that follows a Scikit-Learn API such as Scikit-Learn Models, Xgboost, LightGBM, CatBoost, etc. All in all, the library is an easy-to-use productivity booster that enables fast experiments and helps you focus more on the business problem at hand.
