This is the third post in my series of machine learning techniques and best practices. If you missed the earlier posts, read the first one now, or review the whole machine learning best practices series. Data scientists commonly use machine learning algorithms, such as gradient boosting and decision forests, that automatically build
Ensemble methods are commonly used to boost predictive accuracy by combining the predictions of multiple machine learning models. The traditional wisdom has been to combine so-called “weak” learners. However, a more modern approach is to create an ensemble of a well-chosen collection of strong yet diverse models. Building powerful ensemble models
I’ve often heard people say about weather forecasters “they have the best job…they just report what the models are telling them, and when they’re wrong they can always blame it on Mother Nature throwing us a curve.” While that’s true, this glass-half-empty crowd is failing to appreciate how amazing the
