I’m pretty happy with our models and predictions. I can say that while I was making those 10 posts about those 10 different models, I really started to question my objectives, and life in general and why I was even doing what I was doing. I was so far removed from any tangible predictions and […]
Read More All-NBA Predict #30 – Classifying All-NBA Players (Reviewing Trees for Feature Selection)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 […]
Read More All-NBA Predict #29 – Classifying All-NBA Players (Part XI – Predicting The Players)Jesus christ… we are on our 10th classifier… I’m going to have to make this my last one because I feel like I’m not doing any analysis anymore haha. NOT THAT THIS ISN’T FUN… but I’d like to actually be able to compare some of these methods together to get a better sense of how […]
Read More All-NBA Predict #28 – Classifying All-NBA Players (Part X – Gradient Boosted Trees)We go from one of the most complex models to one of the least complex… K-NN here we go. Not really too much to explain here… We pick the closest observations by euclidean distance and take a vote. If the majority of them are all-NBA, then the observation is all-NBA. If the majority of them […]
Read More All-NBA Predict #27 – Classifying All-NBA Players (Part VIIII – K-Nearest Neighbours)Well, the last 2 posts was probably the most I’ve ever talked about trees in my life. I usually frequent r/nba at least a few times a day, but no joke I accidentally googled ‘r/trees’ a few times just because I had trees on the mind and for some reason found myself much more relaxed […]
Read More All-NBA Predict #26 – Classifying All-NBA Players (Part VII – Support Vector Machines)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 […]
Read More All-NBA Predict #25 – Classifying All-NBA Players (Part VIII – Neural Networks)In the last post, I explored a single decision tree. In this post, I’m going to expand on that single tree to look at the random forest model. Before we get into random forests, we have to understand the error breakdown of a decision tree. Intrinsically, a tree boasts higher variance compared to some other […]
Read More All-NBA Predict #24 – Classifying All-NBA Players (Part VI – Random Forest)Alrighty, so we’ve taken a look at the following classifiers: Manually selected linear boundary, LDA, QDA, and logistic regression. First one, not so scientific. Next 3, a bit of math involved, nothing too crazy. We now jump to one of the most interpretable models that exist. ESL calls the decision tree as close to an […]
Read More All-NBA Predict #23 – Classifying All-NBA Players (Part V – Decision Tree)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% / […]
Read More All-NBA Predict #22 – Classifying All-NBA Players (Part IV – Logistic Regression)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 […]
Read More All-NBA Predict #20 – Classifying All-NBA Players (Part II – Linear Discriminant Analysis)
