In this third post on supervised machine learning classifiers, I'll be talking about one of the oldest and most widely used techniques - decision trees. Decision trees work well with noisy or missing data and are incredibly fast at runtime. They're additionally nice because you can

K-means clustering is a simple method for partitioning $n$ data points in $k$ groups, or clusters. Essentially, the process goes as follows: 1. Select $k$ centroids. These will be the center point for each segment. 2. Assign data points to nearest centroid. 3. Reassign centroid value to be the calculated

Term frequency-inverse document frequency (TF-IDF) vectorization is a mouthful to say, but it's also a simple and convenient way to characterize bodies of text. Due to its simplicity, this method scales better than some other topic modeling techniques (latent dirichlet allocation, probabilistic latent semantic indexing) when dealing with