This tutorial is a step-by-step, beginner-friendly explanation of how you can integrate PyCaret and Gradio, the two powerful open-source libraries in Python, and supercharge your machine learning experimentation within minutes.
Customer retention is one of the primary KPI for companies with a subscription-based business model. Competition is tough particularly in the SaaS market where customers are free to choose from plenty of providers.
PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is known for its ease of use, simplicity, and ability to quickly and efficiently build and deploy end-to-end ML prototypes.
PyCaret is an open-source, low-code machine learning library and end-to-end model management tool built-in Python for automating machine learning workflows. It is incredibly popular for its ease of use, simplicity, and ability to build and deploy end-to-end ML prototypes quickly and efficiently.
This function train all the models available in the model library and scores them using Stratified Cross Validation. The output prints a score grid with Accuracy, AUC, Recall, Precision, F1, Kappa and MCC (averaged accross folds), determined by fold parameter.
PyCaret being a low-code library makes you more productive. With less time spent coding, you and your team can now focus on business problems. PyCaret is a business ready solution. It allows you to do prototyping quickly and efficiently from your choice of notebook environment.
When we approach supervised machine learning problems, it can be tempting to just see how a random forest or gradient boosting model performs and stop experimenting if we are satisfied with the results.
We are excited to announce PyCaret 2.1 — update for the month of Aug 2020. PyCaret is an open-source, low-code machine learning library in Python that automates the machine learning workflow.
I came across Pycaret while I was browsing on a slack for data scientists. It's a versatile library in which you can apply/evaluate/tune many models at once.