Given a supervised machine learning problem, the first question many people ask is usually what is the best classification or regression algorithm to solve it. However, there is no one-size-fits-all solution, or no free lunch. No one could know which algorithm will work the best before trying multiple ones and fine-tuning the optimal one. We will be looking into best practices around this in the following sections.
Best practices in the model training, evaluation, and selection stage
Best practice 15 – choosing the right algorithm(s) to start with
Due to the fact that there are several parameters to tune for an algorithm, exhausting all algorithms and fine-tuning each one can be extremely time-consuming and computationally...