AdaBoost regression uses ensemble learning to improve the performance of numeric prediction models. The video below explains how to use adaBoost with Python.
I’ve been seeing a lot of hot takes on if one should do data science in R or in Python. I’ll comment generally on the topic, and then add my own myopic gear-head micro benchmark. I’ll jump in: If learning the language is the big step: then ... [...Read more...]
I’ve just started experimenting with the Polars data frame library in Python. I really like the programmable API it exposes. In fact I am starting an experimental adapter from the data algebra to Polars. When this is complete one can use the data algebra to run the same data ... [...Read more...]
This week, we’ve been reminding ourselves of some of the amazing talks from the Shiny in Production conference in October. The recordings are now up on our YouTube channel, for anyone to vie...
Deep learning might seem like a challenging field to newcomers, but it’s gotten easier over the years due to amazing libraries and community. PyTorch library for Python is no exception, and it allows you to train deep learning models from scratch on any dataset. Sometimes it’s easier to ...
I was playing around with ChatGPT to see if it could write Python code to generate realistic recruitment process data for me. Here’s the full conversation as I was truly amazed with the results. I emboldened my prompts and had to ask ChatGPT to continue sometimes as longer responses ...
AdaBoost classification is a type of ensemble learning. What this means is that the algorithm makes multiple models that work together to make predictions. Such techniques are powerful in improving the strength of models. The video below explains how to use this algorithm within Python.
Data science and data engineering are incredibly cognitively demanding professions. As data professionals, we are required to leverage both our analytical/engineering skills and our interpersonal ...