Our CEO and founder, Dr. Dan Steinberg recently wrote about gradient boosting machines. Gradient boosting machines are a powerful machine learning technique, and have been deployed with great success over the years in Kaggle competitions.Read More
We recently came across the article, "Random Forest---the go-to machine learning algorithm" from TechWorld Australia.Read More
We recently came across a neat interactive visual introduction to machine learning. It's an excellent explanation on how decision trees work, using data about houses to distinguish homes in New York from homes in San Francisco, for technical and non-technical audiences alike.Recap, taken from the Read More
In order to see how data science can help in discovering our earth’s history, it is important to know firstly, about the Gaia Hypothesis.
When MARS develops a model it actually develops many and presents you with the one that it judges best based on a self-testing procedure. But the so-called MARS optimal model may not be satisfactory from your perspective. It might be too small (include too few variables), too large (include too many variables), too complex (include too many splines, basis functions, or breaks in variables), or otherwise not to your liking based on your domain knowledge. So what can you do to override the MARS process?
Machine learning is already widely established in the sciences and for global applications since many decades. It offers tremendous opportunities and new information, and can bring much progress to the sustainable management of natural resources and human well-being worldwide. But so far, it has been widely underused for science-based wildlife management questions, and which traditionally just ask very narrow and parsimonious questions; often applied just after the fact. Here I review the traditional wildlife analysis design, and how it compares to, and can be extended and updated with, machine learning methods.