A Gentle Introduction to Model Selection for Machine Learning

Given easy-to-use machine learning libraries like scikit-learn and Keras, it is straightforward to fit many different machine learning models on a given predictive modeling dataset.

The challenge of applied machine learning, therefore, becomes how to choose among a range of different models that you can use for your problem.

Naively, you might believe that model performance is sufficient, but should you consider other concerns, such as how long the model takes to train or how easy it is to explain to project stakeholders. Their concerns become more pressing if a chosen model must be used operationally for months or years.

Also, what are you choosing exactly: just the algorithm used to fit the model or the entire data preparation and model fitting pipeline?

In this post, you will discover the challenge of model selection for machine learning.

After reading this post, you will know:

• Model selection is the process of choosing one among many candidate models for a predictive modeling problem.
• There may be many competing concerns when performing model selection beyond model performance, such as complexity, maintainability, and available resources.
• The two main classes of model selection techniques are probabilistic measures and resampling methods.

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