Occam's razor and machine learning - Data Points

In the last instalment of this blog series, we discussed objectives and accuracy in machine learning. And we described two crucial tests for the utility of a machine learning model: The model must be sufficiently accurate and we must be able to deploy the model so that it can produce actionable outputs from the available data. We then introduced a real-world scenario -- predicting train failures up to 36 hours in advance of their occurrence using sensor data -- to illustrate the application of those tests. But how did we decide which of the multitude of machine learning algorithms to use to train our model in the first place? To answer this question, we need to revisit the main classes of machine learning algorithms.