How Feature Engineering Taught Me That Better Data Often Beats Better Algorithms When I first...
How Feature Engineering Taught Me That Better Data Often Beats Better Algorithms
When I first started learning Machine Learning, I believed what many beginners believe:
If my model is not performing well, I need a better algorithm.
So I kept switching models.
I moved from Logistic Regression to Decision Trees, then Random Forest, and later even started reading about XGBoost and Neural Networks.
Dev.to / Medium / Substack I Finished Machine Learning. And Then Changed The...
Not a data scientist, but I've shipped ML models in production across 7 projects. XGBoost regression, 109 Prophet forecasts, SHAP…
If you write code for research, you've felt this: there's almost certainly a model for your problem,...
Just as with LLMs, success in other frontiers of AI will require access to large volumes of high-quality data. That will require…
Stanford and Tsinghua ran a controlled experiment: same model, same task, different harness. A 6x performance gap. Here is what…
A Blog post by Dharma-AI on Hugging Face
