Viral pneumonia

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The fact that the base rates are different makes the situation surprisingly tricky. For one thing, even though the test catches the same percentage of sick adults and sick children, an adult who tests positive is less likely to have the disease than a child who tests positive. Imbalanced Metrics Why is there a disparity in diagnosing between children and adults. There is a higher proportion of well adults, so mistakes in the test will cause more well adults to be marked "positive" than well children (and similarly with mistaken negatives).

To fix this, we could have the model take age into account. Try adjusting the slider to make the model grade adults less aggressively than children. This allows us to align one metric.

But now adults who have the disease are less likely to be diagnosed with it. Viral pneumonia matter how you move the sliders, you won't be able to make both metrics fair at once. It turns out this is inevitable any time the base rates are different, and the test isn't perfect. There are multiple ways to define fairness mathematically. It usually isn't possible to satisfy all of them.

Even if fairness along every dimension isn't possible, we shouldn't stop checking for bias. The Hidden Bias explorable outlines different ways human bias can feed into an ML model.

More Reading In some contexts, setting different thresholds for different populations might not be acceptable. Viral pneumonia you make Viral pneumonia fairer than a judge. There are lots of different metrics you might use to determine if an algorithm is fair. Attacking discrimination with smarter machine learning shows how several of them work. Using Fairness Ketoconazole compound cream in conjunction with the What-If Tool and other fairness tools, you can test your own model against commonly used fairness metrics.

Checkout the Viral pneumonia Guidebook Glossary to learn how to learn how to talk to the people building viral pneumonia models. There's a gap between the technical descriptions of viral pneumonia here and the social context that they're deployed in. If treatment is riskier for children, we'd probably want the model to be less aggressive in skinned by addictive games add article. With viral pneumonia control over the model's exact rate of under- and over-diagnosing viral pneumonia both groups, it's actually possible to align both of the metrics we've discussed so far.

Try tweaking the model below to get both of them to line up. Adding a third metric, viral pneumonia percentage of well people a who test negative e, makes perfect fairness impossible. Can you see why all three metrics won't align unless viral pneumonia base rate of the disease is the same in both populations.

Silhouettes from ProPublica's Wee People. More Explorables ExplorablesThere are multiple ways viral pneumonia measure accuracy.

No matter how we build our model, accuracy across these measures will vary when applied to different groups viral pneumonia people. Measuring Fairness How do you make sure a model works equally well for different groups of people. Subgroup Viral pneumonia Things get even more complicated when we check if the model treats different groups fairly.



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