Regression
Predict quantities with lines, curves and trees.
01 / Lines and evidence
Read a fitted line
Interpret slope and intercept in context.
12–18 min · 3 practicesRMSE and R² answer different questions
Distinguish target-unit errors from relative fit.
12–18 min · 3 practicesRead residual patterns and limits
Use residual structure to question a fitted relationship.
12–18 min · 3 practices02 / Several predictors
Several predictors, conditional comparisons
Interpret one input while holding others fixed.
12–18 min · 3 practicesRead coefficients after encoding
Align coefficients with encoded feature names and a reference category.
12–18 min · 3 practicesRegression evidence retrieval
Retrieve earlier concepts before combining them.
15–20 min · 3 practicesCorrelated predictors and unstable coefficients
Separate coefficient stability from predictive stability.
12–18 min · 3 practices03 / Curves and trees
Make curved features
Understand expansion before fitting.
12–18 min · 3 practicesValidate polynomial flexibility
Choose degree inside the complete pipeline.
18–25 min · 4 practicesTrees predict with leaf averages
Learn recursive splits and regression leaves within this branch.
25–40 min · 4 practicesControl tree complexity
Use depth and minimum leaf size with validation evidence.
12–18 min · 3 practicesFlexibility retrieval
Retrieve earlier concepts before combining them.
15–20 min · 3 practicesWhat fitted explanations cannot establish
Match claim strength to the evidence.
12–18 min · 3 practicesRegression checkpoint
Compare mixed-input linear regression and a tree on Candy with common training folds, a reference, diagnosis and final evidence.
25–35 min · 1 practices