{"id":274,"date":"2020-09-24T19:52:03","date_gmt":"2020-09-24T19:52:03","guid":{"rendered":"https:\/\/machine-learning.webcloning.com\/2020\/09\/24\/how-to-hill-climb-the-test-set-for-machine-learning\/"},"modified":"2020-09-24T19:52:03","modified_gmt":"2020-09-24T19:52:03","slug":"how-to-hill-climb-the-test-set-for-machine-learning","status":"publish","type":"post","link":"https:\/\/salarydistribution.com\/machine-learning\/2020\/09\/24\/how-to-hill-climb-the-test-set-for-machine-learning\/","title":{"rendered":"How to Hill Climb the Test Set for Machine Learning"},"content":{"rendered":"
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Hill climbing the test set<\/strong> is an approach to achieving good or perfect predictions on a machine learning competition without touching the training set or even developing a predictive model.<\/p>\n

As an approach to machine learning competitions, it is rightfully frowned upon, and most competition platforms impose limitations to prevent it, which is important.<\/p>\n

Nevertheless, hill climbing the test set is something that a machine learning practitioner accidentally does as part of participating in a competition. By developing an explicit implementation to hill climb a test set, it helps to better understand how easy it can be to overfit a test dataset by overusing it to evaluate modeling pipelines.<\/p>\n

In this tutorial, you will discover how to hill climb the test set for machine learning.<\/p>\n

After completing this tutorial, you will know:<\/p>\n