R learning: Neural Networks

Artificial neural networks (ANNs) are computing systems inspired by the human brain. They can teach themselves to do tasks, simply by considering examples of the tasks’ outcome. For example, they can learn to identify images that contain cats by analyzing example images that have been tagged “cat” or “no cat”. When given enough examples, the…

Light GBM vs. XGBOOST in Python & R

XGBOOST stands for eXtreme Gradient Boosting. A big brother of the earlier AdaBoost, XGB is a supervised learning algorithm that uses an ensemble of adaptively boosted decision trees. For those unfamiliar with adaptive boosting algorithms, here’s a 2-minute explanation video¬†and a written tutorial. Although XGBOOST often performs well in predictive tasks, the training process can…

Multi-Armed Bandits: The Smart Alternative for A/B Testing

Just as humans, computers learn by experience.The purpose of A/B testing is often to collect data to decide whether intervention A or B is better. As such, we provide one group with intervention A whereas another group receives intervention B. With the data of these two groups coming in, the computer can statistically estimate which…