What is a hill climbing?
An algorithm for iteratively improving ("walking uphill") an ML model until the model stops improving ("reaches the top of a hill").
hill climbing explained in plain English
An algorithm for iteratively improving ("walking uphill") an ML model until the model stops improving ("reaches the top of a hill"). The general form of the algorithm is as follows: Evaluate the new candidate models and take one of the following actions: - If a candidate model outperforms the starting model, then that candidate model becomes the new starting model. In this case, repeat Steps 1, 2, and 3. - If no model outperforms the starting model, then you've reached the top of the hill and should stop iterating. See Deep Learning Tuning Playbook for guidance on hyperparameter tuning. See the Data modules of Machine Learning Crash Course for guidance on feature engineering.
Example
Practitioners refer to hill climbing when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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