際際滷shows by User: SiftScience / http://www.slideshare.net/images/logo.gif 際際滷shows by User: SiftScience / Mon, 01 Aug 2016 21:09:19 GMT 際際滷Share feed for 際際滷shows by User: SiftScience Machine Learning Experimentation at Sift Science /slideshow/machine-learning-experimentation-at-sift/64595348 publicturnupthebayespt-160801210919
Alex Paino, a Software Engineer at Sift Science, discusses how we use machine learning to prevent several types of abusive user behavior for thousands of customers. Measuring the accuracy of the thousands of classifiers used in a manner that correctly represents the value provided to customers is a huge challenge for us. Alex describes how we think about this problem and what we have done to address it. This includes an overview of the various tools and methodologies we employ that allow us to quickly summarize the results of an experiment, break ties in mixed result experiments, and drill into specific models and samples. ]]>
Alex Paino, a Software Engineer at Sift Science, discusses how we use machine learning to prevent several types of abusive user behavior for thousands of customers. Measuring the accuracy of the thousands of classifiers used in a manner that correctly represents the value provided to customers is a huge challenge for us. Alex describes how we think about this problem and what we have done to address it. This includes an overview of the various tools and methodologies we employ that allow us to quickly summarize the results of an experiment, break ties in mixed result experiments, and drill into specific models and samples. ]]> Mon, 01 Aug 2016 21:09:19 GMT /slideshow/machine-learning-experimentation-at-sift/64595348 SiftScience@slideshare.net(SiftScience) Machine Learning Experimentation at Sift Science SiftScience Alex Paino, a Software Engineer at Sift Science, discusses how we use machine learning to prevent several types of abusive user behavior for thousands of customers. Measuring the accuracy of the thousands of classifiers used in a manner that correctly represents the value provided to customers is a huge challenge for us. Alex describes how we think about this problem and what we have done to address it. This includes an overview of the various tools and methodologies we employ that allow us to quickly summarize the results of an experiment, break ties in mixed result experiments, and drill into specific models and samples. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/publicturnupthebayespt-160801210919-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Alex Paino, a Software Engineer at Sift Science, discusses how we use machine learning to prevent several types of abusive user behavior for thousands of customers. Measuring the accuracy of the thousands of classifiers used in a manner that correctly represents the value provided to customers is a huge challenge for us. Alex describes how we think about this problem and what we have done to address it. This includes an overview of the various tools and methodologies we employ that allow us to quickly summarize the results of an experiment, break ties in mixed result experiments, and drill into specific models and samples.
Machine Learning Experimentation at Sift Science from Sift Science
]]> 1128 4 https://cdn.slidesharecdn.com/ss_thumbnails/publicturnupthebayespt-160801210919-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-SiftScience-48x48.jpg?cb=1523454487 Sift Science is the leading provider of real-time machine learning fraud prevention for online businesses across the globe. Every day, thousands of websites and apps rely on Sift Science to eliminate fraud and abuse, streamline and automate fraud-prevention workflows, and provide legitimate users with a seamless experience. Launched in 2011, Sift Science is headquartered in San Francisco, California. siftscience.com