2025-07-25: Feature Engineering with Shallow Features and Methods
Creating synthesized instances with SMOTE (from Figure 3 in Wongvorachan et al. ) Jason Brownlee gave the definition of feature engineering as follows: "feature engineering is the process of transforming raw data into features that better represent the underlying problem to the predictive models, resulting in improved model accuracy on unseen data". In other words, feature engineering is manually designing what the input Xs should be. Feature engineering is regarded as key to success in applied machine learning. " Much of the success of machine learning is actually success in engineering features that a learner can understand" , as pointed out by Scott Locklin . There are many application scenarios for feature engineering, including loan application fraud detection/prevention, recommendation system user behavior modeling and disease diagnosis/risk prediction, etc. In a loan application fraud prevention program, data scientists can decide whether...
