Bayesian Optimization of Machine Learning Models

Many predictive and machine learning models have structural or tuning parameters that cannot be directly estimated from the data. For example, when using K-nearest neighbor model, there is no analytical estimator for K (the number of neighbors). Typically, resampling is used to get good performance estimates of the model for a given set of values for K and the one associated with the best results is used. This is basically a grid search procedure. However, there are other approaches that can be used.