Posted in Machine Learning, Statitics, Theory

Is your model good enough? Evaluation metrics in Classification and Regression

Was working on my research with sklearn, but realized that choosing the right evaluation metrics was always a problem to me. If someone asks me ,”does your model performs well?” The first thing in my mind is “accuracy”. Besides the accuracy, there are a lot, depending on your own problem. Continue reading “Is your model good enough? Evaluation metrics in Classification and Regression”

Posted in Algorithm, Machine Learning, Statitics

Lucky or not: Monte Carlo Method

AlphaGo!

http://c.brightcove.com/services/viewer/federated_f9?isVid=1&isUI=1

When you play any games, probably you have strategies or experiences. But you could not deny that some times you need luck, which data scientists would say a “random choice”. Monte Carlo Method provides only an approximate optimizer, thus giving you the luck to win a game.
Continue reading “Lucky or not: Monte Carlo Method”