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Machine Learning at Indeed: Scaling Decision Trees

This post is also available in: English

This talk was held on 2014年 2月 26日 (水曜日) 19時00分

Decision trees are a widely used machine learning technique for supervised classification. Indeed’s data sets consist of tens of billions of documents with millions of distinct features. Since decision trees back some of our most important features, we built a custom distributed system to efficiently train them. Every day, we now build dozens of decision trees across this data. This same system now powers our internal analytical tools that enable quick data-driven decision-making at Indeed.