Integrating Scikit-Learn and Statsmodels for Regression
Statistics and Machine Learning both aim to extract insights from data, though their approaches differ significantly. Traditional statistics primarily concerns itself with inference, using the entire dataset to test hypotheses and estimate probabilities about a larger population. In contrast, machine learning emphasizes prediction and decision-making, typically employing a train-test split methodology where models learn from a portion of the data (the training set) and validate their predictions on unseen data (the testing set).
In this post, we will demonstrate how a seemingly straightforward technique like linear regression can be viewed through these two lenses. We will explore their unique contributions by using Scikit-Learn for machine learning and Statsmodels for statistical inference.
Let’s get started.