Last Updated on August 15, 2020 You often jump from problem-to-problem in applied machine learning and you need to get up to speed on a new dataset, fast. A classical and under-utilised approach that you can use to quickly build a relationship with a new data problem is Exploratory Data Analysis. In this post you will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use and why you should be performing EDA on your next problem. […]

Last Updated on March 5, 2020 The most commonly reported measure of classifier performance is accuracy: the percent of correct classifications obtained. This metric has the advantage of being easy to understand and makes comparison of the performance of different classifiers trivial, but it ignores many of the factors which should be taken into account when honestly assessing the performance of a classifier. What Is Meant By Classifier Performance? Classifier performance is more than just a count of correct classifications. […]

Last Updated on September 5, 2016 In a recent presentation at MLConf, Xavier Amatriain described 10 lessons that he has learned about building machine learning systems as the Research/Engineering Manager at Netflix. In this you will discover these 10 lessons in a summary from his talk and slides. Lessons Learned from Building Machine Learning Systems Taken from Xavier’s presentation 10 Lessons Learned The 10 lessons that Xavier presents can be summarized as follows: More data vs./and Better Models You might […]

Last Updated on August 15, 2020 In a 2010 post Hilary Mason and Chris Wiggins described the OSEMN process as a taxonomy of tasks that a data scientist should feel comfortable working on. The title of the post was “A Taxonomy of Data Science” on the now defunct dataists blog. This process has also been used as the structure of a recent book, specifically “Data Science at the Command Line: Facing the Future with Time-Tested Tools” by Jeroen Janssens published […]