Master the quantitative logic of uncertainty. Learn marginal, joint, and conditional probability, Bayes' Theorem, and Expected Monetary Value — while building the reflexes to spot base-rate fallacies in automated algorithms and generative AI tools.
Topics and managerial frameworks covered in Probability Foundations, Decision Trees & Risk:
Students manipulate these interactive visual simulations directly inside the chapter reading before answering gated check-in questions:
Students set the base rate and the model's per-class detection ability, then watch the majority-class baseline, the model's accuracy, and balanced accuracy move against each other. The point being made: as the outcome gets rarer, the do-nothing baseline ris...
Students drag a threshold slider and watch a confusion matrix, precision, recall, accuracy, and F1 score update in real-time. Uses 500 pre-generated churn probability scores with known labels.
Teaches: accuracy alone is misleading; threshold choice drives the precision- recall trade-off; the default 0.50 is arbitrary.
Three sliders (FP cost, FN cost, TP revenue) drive a real-time profit curve showing expected profit at each threshold. A vertical line marks the optimal threshold (T*) and contrasts it with the naïve 0.
Teaches: cost asymmetry drives threshold asymmetry; revenue dramatically shifts the optimal operating point; the 0.50 default is almost never right.
This chapter connects directly to these full MktPraxis analytical apps (with pre-loaded business case scenarios, CSV upload, and reproducible Python export):
Explore prior odds, likelihood ratios, and posterior risk through 1,000-icon natural frequency grids and probability trees.