A typical machine learning workflow rarely involves applying one single approach to the problem at hand. Models generally go through an iterative process with various techniques applied and evaluated. Feature engineering strategies are tested, discarded, then revisited; algorithms and their parameters are iterated exhaustively, sometimes for just a fraction of a percentage improvement. This cyclical process of experimentation and refinement is essential in working towards a robust solution.
The following article is a demonstration of a typical workflow in preparing, testing, comparing, and scoring a classification model for a given problem. In this example, the product team of a hypothetical cooking website is attempting to improve their current system of selecting recipes for the website’s front page, by implementing a machine learning system based on past performance of recipes they’ve manually selected. To that end, two algorithms are applied — a Logistic Regression…