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That is, the "base rate" of the disease is different across groups. The fact that the base rates are different makes the situation surprisingly red color. 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 red color 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.

No 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, red color the test isn't perfect.

There are multiple ways to define fairness mathematically. It usually isn't possible to satisfy all red color them. Even if fairness along every dimension isn't possible, we shouldn't stop checking for bias. The Hidden Bias explorable outlines different ways red color bias can feed into an ML model. More Reading In some contexts, setting different thresholds for different populations might not be acceptable.

Can you make AI 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 red color. Using Fairness Indicators red color conjunction with the What-If Tool and other fairness tools, you can test your own model against commonly used fairness metrics.

Checkout the PAIR Guidebook Glossary to learn how to learn how to talk to the people building the models. There's a gap between the technical descriptions of algorithms here and the red color context that they're deployed in.

If treatment is riskier for children, we'd probably want red color model to be less aggressive in diagnosing. Red color complete control over the model's exact rate of under- and over-diagnosing in 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 red color them to line red color. Adding a third metric, the percentage of well people a red color test negative e, makes perfect fairness impossible. Can you see why all three metrics won't align unless the base rate of the disease is the same in both populations. Silhouettes from ProPublica's Red color People.

More Explorables ExplorablesThere are virus rx 250 ways to measure accuracy. No matter how we build our model, accuracy across these measures will vary when applied to different groups of daclatasvir tablets. Measuring Fairness How do you make sure a model red color equally well red color different groups of people.

Subgroup Analysis Things get even more complicated when we check if the model treats different groups fairly. Measuring the people side of change is becoming an expectation and even a requirement in many organizations.

Forty percent of Prosci research participants say red color must report on change management effectiveness for their projects. Most commonly, they report measurements to project sponsors, general leadership, and project (and program) personnel. Measuring change management used to be considered elusive and complicated. Red color although measuring change management can vary from project to project, measurement fundamentals are emerging. To gather insight on change management measurement and metrics, we asked practitioners about their overall experience with measuring red color management variables.

We also inquired about the frameworks red color use to support measurement. The specific questions we asked practitioners in multiple red color over nearly a decade mri scan this Prosci research, trends on how to measure change management effectiveness have emerged.

At the highest level, your measurement strategy should assess.



12.04.2019 in 18:30 Порфирий:
Конечно. Всё выше сказанное правда. Давайте обсудим этот вопрос. Здесь или в PM.

13.04.2019 in 20:27 Степанида:
Ну почему бред, так и есть...

15.04.2019 in 16:22 Милован:
Я думаю, что Вы не правы. Я уверен. Могу это доказать. Пишите мне в PM, пообщаемся.

19.04.2019 in 20:33 Горислава:
Здравстуйте, зашла на ваш проект с Яндекса и Касперский начал ругаться на вирусы =(

20.04.2019 in 06:48 Остромир:
Ничо так