Samuele Mazzanti

Here’s a better framework for data-driven decision-making Samuele Mazzanti · Follow Published in Towards Data Science · 9 min read · 11 hours ago — [Image by Author] Data scientists are in the business of decision-making. Our work is focused on how to make informed choices under uncertainty. And yet, when it comes to quantifying that uncertainty, we often lean on the idea of “statistical significance” — a tool that, at best, provides a shallow

Linear models can handle non-linear relationships thanks to data pre-processing. But how close can they get to more sophisticated models? Samuele Mazzanti · Follow Published in Towards Data Science · 8 min read · 2 hours ago — [Image by Author] Some weeks ago, I published a post on LinkedIn. The post was based on the following figure, comparing the predictions made by two models: Linear Regression, and CatBoost. [Image by Author] The gist of

… No, the solution is not “Adjusted R-Squared” Samuele Mazzanti · Follow Published in Towards Data Science · 10 min read · 8 hours ago — [Image by Author] R-Squared is one of the most popular metrics to evaluate regression models. It’s taught in any statistics class and it’s one of the metrics implemented in Scikit-learn. However, some doubts have been raised about the reliability of this metric. In the notes for his course at

How simulations outperform traditional stats in that they are easier to understand, more flexible, and economically meaningful Samuele Mazzanti · Follow Published in Towards Data Science · 11 min read · 9 hours ago — [Image by Author] Controlled experiments such as A/B tests are used heavily by companies. However, many people are repelled by A/B testing due to the presence of intimidating statistical jargon including terms such as “confidence”, “power”, “p-value”, “t-test”, “effect size”,