Simply Salford Blog

The Shape of the Trees in Gradient Boosting Machines

Posted by Salford Systems on Fri, Mar 25, 2016 @ 01:09 PM

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.

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Topics: TreeNet, stochastic gradient boosting, machine learning, gradient boosting machine, Jerome Friedman, gradient boosting, gradient boosting machine learning

Random Forests: The Machine Learning Algorithm

Posted by Salford Systems on Thu, Mar 3, 2016 @ 10:56 AM

We recently came across the article, "Random Forest---the go-to machine learning algorithm" from TechWorld Australia.

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Topics: RandomForests, Random Forest, Random Forests, bootstrap sampling, classification, Regression, classification trees, machine learning, regression trees

Machine Learning [Visualization]

Posted by Salford Systems on Fri, Feb 26, 2016 @ 08:51 AM

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

Topics: overfitting, machine learning, decision trees, decision tree

How Data Science Can help us Discover our Planet’s History

Posted by Kimberly Fahrnkopf on Wed, Oct 15, 2014 @ 06:55 AM

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. 

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Topics: data mining, data science, predictive modeling, machine learning

Choosing Your Own Preferred MARS Model

Posted by Dan Steinberg on Wed, Aug 20, 2014 @ 09:46 AM

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?

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Topics: data mining, Variable Importance, MARS, data science, predictive modeling, predictive model, data analysis, Dan Steinberg, statistics, machine learning

Paradigm Shifts in Wildlife Through Data Mining and Machine Learning

Posted by Heather Hinman on Wed, Nov 7, 2012 @ 07:53 AM

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.

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Topics: machine learning, wildlife

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