All-NBA Predict #29 – Classifying All-NBA Players (Part XI – Predicting The Players)

So I’ve just spend the last… Jesus… 10 posts talking about random mumbo jumbo… Sensitivity, specificity, AUC, ROC… But I really haven’t actually said “Hey, Lebron’s probably going to be all-NBA”. Don’t need an algorithm for that one, fortunately, but what about… one Paul George? And I say that basically cheating in hindsight because the […]

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All-NBA Predict #25 – Classifying All-NBA Players (Part VIII – Neural Networks)

Alright, we’re running out of classifiers for us to try here. We’ve already gotten some pretty decent results thus far. My personal favourite has probably been just a single decision tree or the random forest. The interpretability of those models are pretty cool. LDA and QDA are also pretty neat because of their simplicity, but […]

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All-NBA Predict #22 – Classifying All-NBA Players (Part IV – Logistic Regression)

Okay. So last time, we tried looking at LDA and QDA in addition to our manually chosen linear decision boundary to classify if players are all-NBA team worthy or not. We got the following results: Manually chosen linear decision boundary – 93% / 92% accuracy for all-NBA / non all-NBA respectively LDA – 69% / […]

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All-NBA Predict #20 – Classifying All-NBA Players (Part II – Linear Discriminant Analysis)

Okay, so last time, I selected two boundaries – one a rectangle, one a linear line. Anything on one side of the shape was considered all-NBA, anything on the other was considered non all-NBA. Did it work? For the rectangle, no. Well… I guess I didn’t really try to optimize it, but it was clear […]

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