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In this chapter we explore machine learning. This topic is closely related to statistical modeling, which we considered in Chapter 14, in the sense that both deal with using data to describe and predict outcomes of uncertain or unknown processes. However, while statistical modeling emphasizes the model used in the analysis, machine learning side steps the model part and focuses on algorithms that can be trained to predict the outcome of new observations.
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In practice it is common to work with both statsmodels and scikit-learn, as they, in many respects, complement each other. However, in this chapter we focus solely on scikit-learn.
However, note that we can never be sure that a machine learning application does not suffer from overfitting before we see how the application performs on new observations, and a repeated reevaluation of the application on a regular basis is a good practice.
- Machine Learning
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- Chapter 15