District Data Labs - Visual Diagnostics for More Informed Machine Learning: Part 3

Note: Before starting Part 3, be sure to read Part 1 and Part 2! In this final installment of Visual Diagnostics for More Informed Machine Learning, we'll close the loop on visualization tools for navigating the different phases of the machine learning workflow. Recall that we are framing the workflow in terms of the'model selection triple' -- this includes analyzing and selecting features, experimenting with different model forms, and evaluating and tuning fitted models. So far, we've covered methods for visual feature analysis in Part 1 and methods for model family and form exploration in Part 2. This post will cover evaluation and tuning, so we'll begin with two questions: You've probably heard other machine learning practitioners talking about their F1 scores or their R-Squared value. Generally speaking, we do tend to rely on numeric scores to tell us when our models are performing well or poorly. There are a number of measures we can use to evaluate our fitted models.