Classical machine learning models

Classical ML usually means proven model families that work great on tables of features: linear/logistic regression, decision trees, random forests, gradient boosting, k-nearest neighbors, SVMs, and friends.

Not every problem needs a giant neural net. On many business spreadsheets, classical models are faster to train, easier to interpret, and strong enough.

• Linear models: weighted sums of features
• Trees: ask yes/no questions to split data
• Ensembles: combine many trees to reduce error

Pick simply first, measure, then escalate complexity.

Predicting customer churn from plan type, tenure, and tickets often starts with logistic regression or gradient boosting—not an LLM.

List a prediction problem at work. Would your inputs fit in a spreadsheet? If yes, classical ML is a first stop.

⚠️ Myth: Deep learning made classical ML obsolete.

✓ Reality: Tabular benchmarks often still favor boosted trees.

⚠️ Myth: Interpretability means the model is fair.

✓ Reality: Interpretable models can still encode bias (Course 19).

• scikit-learn algorithm chooser / user guide: https://scikit-learn.org/stable/user_guide.html ↗
• ANN live: https://www.ainerdnetwork.com/learn/classical-ml-models ↗
• Google ML Crash Course: https://developers.google.com/machine-learning/crash-course ↗