Starting a new machine learning project brings a rush of enthusiasm, and it might be quite tempting to jump straight into the deep end. There are plenty of the latest cutting-edge models or complex algorithms that you might have read about. They promise groundbreaking results, and avoiding the temptation to experiment with them right off the bat is a tough task. Any modern entrepreneur is eager to test state-of-the-art techniques and showcase sophisticated projects to the community.
A baseline gives that essential reference point — it is straightforward, quick to build, and inherently explainable. Surprisingly, often a baseline model, which may only take 10% of the total development effort, can achieve up to 90% of the desired performance, producing a highly efficient path to reasonable results. no simpler point of reference. no simpler point of reference.