Techniques to address overfitting, hyperparameter tuning, and model interpretability.
I’m always on the lookout for ideas that can improve how I tackle data analysis projects. I particularly favor approaches that translate to tools I can use repeatedly. Most of the time, I find these tools on my own—by trial and error—or by consulting other practitioners. I also have an affinity for academics and academic research, and I often tweet about research papers that I come across and am intrigued by. Often, academic research results don’t immediately translate to what I do, but I recently came across ideas from several research projects that are worth sharing with a wider audience.
The collection of ideas I’ve presented in this post address problems that come up frequently. In my mind, these ideas also reinforce the notion of data science as comprising data pipelines, not just machine learning algorithms. These ideas also have implications for engineers trying to build artificial intelligence (AI) applications. Continue reading “3 ideas to add to your data science toolkit”→