This post addresses a common data science task – comparing multiple models – and explores how you might do this when you’re running the models in R's caret package. We’ll work with the same data set and objective as the last post, which involved predicting which customers would respond to a marketing campaign, and build on that post by making one of the models we’re comparing a neural network. The other models I’m adding here are a random forest and a logistic regression.Read More
Neural networks are a great analytic tool for generating predictions from existing data. They can detect complex, non-linear relationships in data (including interactions among predictors), can handle large datasets with many predictors, and often produce more accurate predictions than regression/logistic regression. As with random forests, they can be used for regression or classification.
For this post, I take on a classic classification challenge and seek to answer the question: which customers are most likely to respond to a marketing campaign?Read More