Lorempixel

Lorempixel

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Game Theory Optimal

Approximating Game-Theoretic Optimal Strategies
Cepheus and Computer Poker Algorithms
GTO vs Exploitative Play
Safe and Nested Subgame Solving
Depth-Limited Solving

Mailer Campaign Uplift Modeling

Profit(C) = ExpectedProfit(C) x [P(B | V) - P(B | C)] - AdCost(C)

• P(B | C) - probability of buying given control without ad campaign (Naive Bayes)
• ExpectedProfit(C) - profit to make from customer if they decide to buy (Regression)
• P(B | V) - probability of buying given variant of ad campaign (Naive Bayes)
• AdCost(C) - cost to mail campaign to customer as a constant
• likely to take into account market or customer segmentation
• regression could be either logistic or linear
• total profit would be determined by how much the customer decided to buy either with control and/or ad campaign
• optimization of ad campaign given the customer conversion ratio
• use customer data as part of expected profit measures for average spend
• additionally, more ways to approach the same contextual measures of profit

uplift modelling

Test-Driven Machine Learning

TDD -> Kent Beck
BDD -> Dan North
Refactoring -> Martin Fowler
Agile -> James Shore

Random processes in machine learning need to be measured and controlled, various simple testing strategies can make this possible.