TPE Algorithm

The tree-structured parzen estimator (TPE) algorithm uses the Gaussian mixture model to learn the model hyperparameters. On each trial, for each parameter, TPE fits one Gaussian mixture model l(x) to the set of parameter values associated with the best objective values, and another Gaussian mixture model g(x) to the remaining parameter values. It chooses the hyperparameter value that maximizes the ratio l(x)/g(x).

**Table 1** TPE algorithm parameters
Parameter	Description	Recommended Value
num_samples	Number of times to sample from the hyperparameter space	The value is an integer ranging from 10 to 20. The larger the value, the longer the search time and the better the effect.
n_initial_points	Number of random evaluations of the objective function before starting to approximate it with tree parzen estimators	The value is an integer. You are advised not to change it.
gamma	Quantile of the TPE algorithm to divide l(x) and g(x)	This value is a float ranging from 0 to 1. You are advised not to change it.