Demo 04
Classic Machine Learning: How do machines learn decision boundaries from data?
Classic machine learning shifted from hand-written rules to data-driven methods. This chapter shows the relationship among examples, model complexity, and decision boundaries.
Teaching interaction
Classic Machine Learning: How do machines learn decision boundaries from data?
How do machines learn classification boundaries from data?
A linear model learns a simple dividing line
A linear boundary is stable and interpretable, but its expressive power is limited.
- Understand how classic machine learning learns a decision boundary from examples.
- Compare the inductive biases of linear, nonlinear, and overfit boundaries.
- Observe how an outlier can affect a model boundary and its generalization risk.
This demo uses preset two-dimensional points and boundaries to illustrate inductive bias. It does not train a real model or represent actual classifier performance.
Observation guide
A model learns a boundary from examples instead of memorizing rules
The earlier problem was that rules could not cover every situation. Classic machine learning learned patterns from data, but it did not automatically solve feature design, generalization, bias, or overfitting. Its follow-on influence includes SVMs, tree models, ensemble learning, and deep learning.
References