Optimization

Step through boosting rounds on noisy 1D data and see shrinkage and tree depth trade convergence speed for stability.

Drag a starting point across a bowl, a ravine, and a Rosenbrock valley and watch gradient descent, momentum, RMSProp, and Adam disagree.

Retrain a linear classifier under class weighting, oversampling, undersampling, and SMOTE, and watch accuracy diverge from minority recall.