The SAS Data Science Blog
Advanced analytics from SAS data scientists
In the field of machine learning, online learning refers to the collection of machine learning methods that learn from a sequence of data provided over time. In online learning, models update continuously as each data point arrives. You often hear online learning described as analyzing “data in motion,” because it
This is the first in a series of posts about machine learning concepts, where we'll cover everything from learning styles to new dimensions in machine learning research. What makes machine learning so successful? The answer lies in the core concept of machine learning: a machine can learn from examples and
In my 25 years at SAS, I‘ve noticed the continued use of important algorithms, such as logistic regression and decision trees, which I’m sure will continue to be steady staples for data scientists. After all, they’re easy-to-use, interpretable algorithms. However, they’re not always the most accurate and stable classifiers. To
This is the final post in my series of machine learning best practices. If you missed the earlier posts, start at the beginning, or read the whole series by clicking on the image to the right. While post four in the series was about combining different types of models, this
This is the seventh post in my series of machine best practices. Catch up by reading the first post or the whole series now. Generalization is the learned model’s ability to fit well to new, unseen data instead of the data it was trained on. Overfitting refers to a model that fits
In the recent article, “Price-bots can collude against consumers,” the Economist discusses the consumer effects of prices set by price-bots. The article starts with an example of gasoline pricing strategies on Martha’s Vineyard. With a small number of gas stations on the island, the price-bots can cover all competitor prices frequently