Feature engineering, the process creating new input features for machine learning, is one of the most effective ways to improve predictive models. Coming up with features is difficult, time-consuming, requires expert
Imbalanced classes put "accuracy" out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. Standard accuracy no longer reliably measures performance,
Overheard after class: "doesn't the Bias-Variance Tradeoff sound like the name of a treaty from a history documentary?" Ok, that's fair... but it's also one of the most important concepts to understand
Welcome to Part 2 of our tour through modern machine learning algorithms. In this part, we'll cover methods for Dimensionality Reduction, further broken into Feature Selection and Feature Extraction. In general,
In this guide, we'll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don't really explain the practical tradeoffs of each algorithm, which we hope to do
Can you guess the answer to this riddle? If you've studied machine learning, you've seen this everywhere...If you're a programmer, you've done this a thousand times...If you've practiced any skill, this is already second-nature for
Recently, some of our readers have been asking us about the best programming language for data science. Immediately, R and Python both come to mind... but which of these two