这是什么
Regression analysis, in a data science context, builds predictive models (from simple linear regression to logistic regression for classification) directly from real data — covering feature selection, splitting data into training and test sets, and evaluating a model with metrics like R² and RMSE rather than proving formal statistical assumptions.
为什么重要
This is the practical, applied side of regression that data science and machine learning roles actually use day to day — the emphasis shifts from proving a model satisfies textbook statistical assumptions to building a model that predicts well on genuinely unseen data, which is a distinct and equally important skill.
考试小贴士
Always evaluate a regression model on a held-out test set, never on the same data it was trained on — a model that fits its training data almost perfectly but performs poorly on new data has overfit, and this is the single most common mistake in applied regression work.