## Tags » All-NBA

#### HotSportsTalk Podcast Episode 1- Cavs Struggles

All NBA

Before the NBA trade deadline ended at three o’clock yesterday afternoon, the Cleveland Cavaliers have decided to part ways with six of their players. These players include: Isaiah Thomas, Channing Frye, Iman Shumpert, Derrick Rose, Jae Crowder, and Lebron James good friend Dwayne Wade. 372 more words

Cleveland

#### An All-Time, All-Rapper, All-NBA Team

We’ve arrived at that magical point of the summer where, well, not much happens. Of course the current US presidential administration and North Korea can keep folks on their toes, but the lifestyle/leisure media world runs a bit slow. 1,006 more words

Hip-Hop

#### All-NBA Predict #30 - Classifying All-NBA Players (Reviewing Trees for Feature Selection)

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. 5,700 more words

Python

#### All-NBA Predict #27 - Classifying All-NBA Players (Part VIIII - K-Nearest Neighbours)

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. 655 more words

Python

#### 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. 2,638 more words

Python

#### All-NBA Predict #21 - Classifying All-NBA Players (Part III - Quadratic Discriminant Analysis)

Last post, we reviewed a few different topics

• Gaussian distribution
• Multivariate gaussian distribution
• Linear discriminant analysis

We left off feeling a bit unsatisfied with LDA because were were getting 92% / 69% accuracy in prediction for all-NBA players and non all-NBA players respectively. 1,073 more words

Python