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9 𝐃𝐢𝐬𝐭𝐚𝐧𝐜𝐞 𝐌𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞
Many #machinelearning algorithms, whether supervised or unsupervised, make use of distance measures.
Take k-NN for example, a technique often used for supervised learning. As a default, it often uses euclidean distance.
By itself, a great distance measure.
Knowing when to use which distance measure can help you go from a poor classifier to an accurate model.
Study: https://towardsdatascience.com/9-distance-measures-in-data-science-918109d069fa
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SC1, you a douche!
Honestly not very hard. Studied 2 weeks 2 years or of undergrad and scored high enough to get into two top schools for my area of focus.. including ivy league (not that that says everything about a school)
Easy. I got an 822.
I was a pretty good standardized test taker, 30 ACT score and 3.5 college gpa, but was 5 years removed from school and traveling every week and really struggled even with a prep class. Got up to 680 in practice tests but got 600 actual (and cancelled it). It’s not so much the material is hard- it’s all pretty basic algebra and geometry. It’s that you need to learn to recognize patterns and shortcuts and avoid traps, which takes legit practice unless you’re a wiz And if you miss a couple medium or medium-hard questions early, you’re fucked.
At 21, I took it without studying just to see what I could get and scored 770. I didn’t think it was too difficult, but I’m sure if I took it now i wouldn’t do as well.