Python-bloggers

In our last article, we explored the S4 OOP system in R. Up until this point, we had only discussed functional OOP systems in R. Today, we are going to learn about two encapsulated OOP systems available for R: Reference Classes –  introduced to R in 2010 in version 2.12.0 (source). Sometimes also ...

When developing R Shiny apps, making sure they work well and are reliable is important. Rigorous testing isn’t just about finding bugs; it’s about preventing them, saving time, and ensuring everything runs smoothly. We did the comparison of shinytest2 and Cypress. Check out this blog post to learn ...

In this article we look at how to use the MySQL EXPLAIN and EXPLAIN ANALYZE commands to optimise your queries.  We review the syntax these commands use, interpreting their output and how tools like MySQL Workbench and dbForge Studio for MySQL be used in conjunction with them.  To do this ...

The video below shows how to conduct a t-test and an ANOVA analysis using the pingouin library from Python.

Auto XGBoost, Auto LighGBM, Auto CatBoost, Auto GradientBoosting

Ahh, SHAP. As you know it’s become one of the leading frameworks for explaining ML model predictions. I’d guess it’s popularity is due to its appealing theoretical basis, its universal applicability to any type of ML model, and its easy-to-use python package. SHAP promises to turn your ...

Rhino, the R package development framework created by Appsilon, has recently released two updates – versions 1.8 and 1.9. In this fireside chat, Kamil Żyła (one of the core developers of Rhino) and Marek Rogala (Appsilon’s Head of Technology) sit down to discuss these updates and the vision behind Rhino. Watch ...

In today’s data-driven world, the integration of data analytics in various sectors has proven to be transformative, and the field of education is no exception. The ability to systematically analyse large volumes of data offers unprecedented insights and solutions, particularly in the realm of educational investigations. As educational institutions ...

Machine Learning is transforming how we design drugs, model diseases, develop treatments, and conduct clinical trials. We recently collaborated with IIMCB to carry out augmented RNA-Ligand binding prediction with machine learning. Learn more about our work in this blog post. These advancements are helping researchers and healthcare professionals make smarter ...