Extract, Explore, Transform, Model: How Machine Learning Projects Unfold

In the majority of cases, you should compare the strength of a few algorithms before choosing the final model. The decision is not a trivial one, but boils down to finding an algorithm that balances strong performance with accomplishing the task in the most straightforward way. You can compare the strength of algorithms by handing your data to each of them and scoring their performance on the task with a technique called cross-validation. The metric used to score performance will vary by context, but some common ones are R-squared for regression, and accuracy, precision, and recall for classification. For classification tasks, quantifying the cost of both a false positive prediction and a false negative prediction can help you to choose the most appropriate metric.