Package smile.hpo
package smile.hpo
Hyperparameter optimization. Hyperparameter optimization or tuning is
the problem of choosing a set of optimal hyperparameters for a learning
algorithm. A hyperparameter is a parameter whose value is used to control
the learning process. Hyperparameter optimization finds a tuple of
hyperparameters that yields an optimal model which minimizes a predefined
loss function on given independent data. The objective function takes
a tuple of hyperparameters and returns the associated loss. Cross
validation is often used to estimate this generalization performance.
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