3. People often look down on machine learning because in some settings, it can only improve things in small increments.

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3 People Often Look Down On Machine Learning Because In Some Settings It Can Only Improve Things In Small Increments 1 (134.33 KiB) Viewed 25 times

3. People often look down on machine learning because in some settings, it can only improve things in small increments. While this might be true, in many settings a slight change can have a huge impact. As an example, Americans spend about 3 trillion dollars per year spread across 30 billion credit card transactions. Suppose that 0.1% (0.001, as a decimal) of these transactions are fraudulent, and hence, credit card companies lose money reimbursing their users. If machine learning could help reduce the percentage of fraudulent claims even slightly, this would save companies billions of dollars! Researchers at Visa have designed a new algorithm to predict fraud and are curious if it has reduced illegal card usage. You are tasked with determining whether this claim is statistically reasonable using an approximate test with a=0.03. a. What is the smallest number of transactions you could look at and meet Larsen & Marx's re- quirements for using the approximate test? b. Suppose you end up looking at 2400 claims. What is the largest number of fraudulent claims that could appear among those 2400 claims that would cause a move to the alternative hypothesis? (Continue to think about the Larsen & Marx criterion as in part a.)