Choosing a model

Let's say you've defined a task and you have a dataset. What's next? Now you need to choose a model and train it on the dataset to perform that task.

The model is the central concept in ML . ML is basically a science of building models of the real world using data. The term model refers to the phenomenon being modeled, while map refers to the real territory. Depending on the situation, it can play a role of good approximation, an outdated description (in a swiftly changing environment), or even self-fulfilled prophecy (if the model affects the modeled object). "All models are wrong, but some are useful" is a well-known proverb in statistics.