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[2014/11/30: Updated the L1-norm vs L2-norm loss function via a programmatic validated diagram. Thanks readers for the pointing out the confusing diagram. Next time I will not draw mspaint but actually plot it out.] While practicing machine learning, you may have come upon a choice of the mysterious L1 vs L2. Usually the two decisions are : 1) L1-norm vs L2-norm loss function; and 2) L1-regulariza
Did your rank just drop sharp in the private leaderboard in a Kaggle competition? I’ve been through that, too. We all learn about overfitting when we started machine learning, but Kaggle makes you really feel the pain of overfitting. Should I have been more careful in the Higgs Boson Machine Learning competition, I would have selected a solution that would gave me a rank 4 than rank 22. I vow to c
** What is the Class Imbalance Problem? It is the problem in machine learning where the total number of a class of data (positive) is far less than the total number of another class of data (negative). This problem is extremely common in practice and can be observed in various disciplines including fraud detection, anomaly detection, medical diagnosis, oil spillage detection, facial recognition, e
[edit 2014/04/19: Some mistakes were made, but the interpretation follows. Sorry about that.] TL;DR Use Precision Recall area under curve for class imbalance problems. If not, Receiver Operating Characteristic area under curve otherwise. Introduction When being confronted with the class imbalance problem, accuracy is a wrong metric to use. Usually, there are two candidates as metrics: Receiver Ope
[edit: last update at 2014/06/27. My apologies, have been very busy the past few months.] We learn more from code, and from great code. Not necessarily always the 1st ranking solution, because we also learn what makes a stellar and just a good solution. I will post solutions I came upon so we can all learn to become better! I collected the following source code and interesting discussions from the
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