FAQ: Data Analyses and Conclusions - Challenges in predictive analysis

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This community-built FAQ covers the "Challenges in predictive analysis " exercise from the lesson “Data Analyses and Conclusions”.

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FAQs on the exercise _Challenges in predictive analysis _

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The animation is not really necessary here, and brings nothing other than detracting from being able to read the flow chart and understand it. Recommendation to just leave the entire thing as one image…

5 Likes
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Hey, Codecademy staff, can you review how useful the image is for us? It’s a bit confusing, distracting and annoying.

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I think this would be a good course suggestion.
So, create a topic in the Feedback & requests” category under “suggestions & features.”

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Sharing my answers and see what others think.

1. Is the IB test score predicting algorithm high or low risk?
High risk as this determines which college they can be accepted into.

2. Who might be more likely to be accidentally discriminated against by the model: students at large schools with a long history of students taking the exam or students at small schools that only recently began offering the IB program?
Students at small schools that only recently began offering the IB program as there is not enough sample data to offer a fair and accurate result.
3. Is it fair to grant or deny college acceptance based on algorithm-predicted test scores?
Unfair.

1 Like
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I agree with you on all the points.
Perhaps worth to flag that the answer of point 2 could also depends on how the model is built? For example the model could keep count of students that have onlt recently started the IB program and assign them a different score.