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Key takeaways
- The post recaps a three act talk on Gradient Boosting Machine featuring Stanford's Trevor Hastie, 0xdata CTO Cliff Click, and Netflix data scientist Antonio Molins.
- The post describes boosting as a strategy that sequentially applies a classification algorithm to reweighted training data and takes a weighted majority vote.
- The post quotes Trevor Hastie referencing his 2001 book Elements of Statistical Learning, coauthored after being drawn into machine learning through neural networks.
- The post states Antonio Molins, a data scientist at Netflix, uses GBM in his practice of building marketing algorithmic models.
- The post links to 0xdata's GBM implementation on GitHub and the R gbm package on CRAN.
- Act I: Trevor Hastie, Of Stanford Mathematical Sciences, the mathematician behind Lasso & GBM speaks of the nuances of the Algorithm.
- Act II: Cliff Click, CTO of 0xdata, the implementor of parallel and distributed GBM.
- Act III: Antonio Molins, Data Scientist at Netflix, who uses GBM in his practice of data science for Marketing Algorithmic Models.
Boosting is a simple strategy that produces dramatic improvement in prediction performance. It works by sequentially applying a Classification Algorithm to reweighted versions of training data and taking the weighted majority vote of the sequence of classifiers produced.
“In the last 10 years my colleagues and I have been drawn into the machine learning domain, probably after the lure of neural networks. This has led us to offer a statistical perspective on novel and popular techniques arising outside of statistics, such as boosting and support-vector machines. This culminated in our 2001 book “Elements of Statistical Learning”, but the interest continues.”
-Trevor Hastie, http://www.stanford.edu/~hastie