Feature Engineering & Selection
A good feature is worth a thousand data points. Implement numerical transforms (standardization, min-max scaling, log transform, binning) and explain when each is appropriate. Build one-hot, label, and target encoding for categorical features and identify the data leakage risk in target encoding. Construct a TF-IDF vectorizer from scratch and explain why it outperforms raw word counts for text classification. Apply filter-based feature selection (variance threshold, correlation, mutual information) to reduce dimensionality. You have a dataset. You pick an algorithm. You train it. The results are mediocre. You try a fancier algorithm. Still mediocre. You spend a week tuning hyperparameters. Marginal improvement. Then someone transforms the raw data into better features and a simple logistic regression beats your tuned gradient-boosted ensemble. This happens constantly. In classical ML, the representation of the data matters more than the choice of algorithm. A house price model with "square footage" and "number of bedrooms" will beat a model with "address as a raw string" no matter how sophisticated the learner is. The algorithm can only work with what you give it. Feature engineering is the process of transforming raw data into representations that make patterns easier for models to find. Feature selection is the process of throwing away features that add noise without adding signal. Together, they are the highest-leverage activity in classical ML. Raw…
Feature Engineering & Selection: A good feature is worth a thousand data points. Implement numerical transforms (standardization, min-max scaling, log…
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