Dr. Grant Humphries, from the Zoology department at the University of Otago, New Zealand, has spent the last three years studying how a bird species called Sooty Shearwaters can help predict upcoming El Niño occurrences. After much time and research, he has figured out a way to do so using data mining.
We recently had a question about running a model using GPS, and wanted to share the answer in case anyone else has the same issue.
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?
Predictive accuracy is repeatedly cited by data scientists as one of the most important demands in modern data mining algorithms and software. It stands right along side the importance of model-building speed, missing value handling, and memory efficiency. So, if it is so important, how do the experts TEST the accuracy of their models?
Let's get right to it! You're a beginner, and you want to know what is needed to start data mining and become an experienced data scientist overnight. We get it - this is the world we live in - quick and dirty. So here we go, take notes!