# Best practices in the model training, evaluation, and selection stage

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, and no free lunch. No one could know which algorithm will work 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 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 expensive. We should instead shortlist one to three algorithms to start with using the general guidelines that follow (note we herein focus on classification, but the theory transcends to regression, and there is usually a counterpart...