How to regularize your model when testing scores aren’t up to par — When I first began working on data science projects, I was completely focused on getting the best training scores possible. I believed the higher my training accuracy, the better my model was performing, which is only true to an extent. Often, a model’s purpose is to be used on future…

Accuracy, precision, recall and F1 score explained in plain English — When it comes to classification models, the metrics used to determine performance are quite different from those used to evaluate regression models. In my opinion, the metrics used to describe the performance of a classification model are more intuitive. You have likely had experience with some of these metrics when…

The basics of when to use and how to interpret When building a linear regression model, we sometimes hit a roadblock and experience poor model performance and/or violations of the assumptions of linear regression — the dataset in its raw form simply does not perform well. When this occurs, a…

While working on my latest data science project, I realized I needed a way to quickly highlight the maximum value in each of my visualizations, so my reader could focus on the conclusion to each of my analyses. I was using seaborn plots in my project, drawn to their simplicity…