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.. currentmodule:: skopt
.. _minimize-functions:
``skopt``'s top level minimization functions
============================================
These are easy to get started with. They mirror the ``scipy.optimize``
API and provide a high level interface to various pre-configured
optimizers.
:class:`dummy_minimize`
-----------------------
Random search by uniform sampling within the given bounds.
:class:`forest_minimize`
------------------------
Sequential optimisation using decision trees.
A tree based regression model is used to model the expensive to evaluate
function `func`. The model is improved by sequentially evaluating
the expensive function at the next best point. Thereby finding the
minimum of `func` with as few evaluations as possible.
:class:`gbrt_minimize`
----------------------
Sequential optimization using gradient boosted trees.
Gradient boosted regression trees are used to model the (very)
expensive to evaluate function `func`. The model is improved
by sequentially evaluating the expensive function at the next
best point. Thereby finding the minimum of `func` with as
few evaluations as possible.
:class:`gp_minimize`
--------------------
Bayesian optimization using Gaussian Processes.
If every function evaluation is expensive, for instance
when the parameters are the hyperparameters of a neural network
and the function evaluation is the mean cross-validation score across
ten folds, optimizing the hyperparameters by standard optimization
routines would take for ever!
The idea is to approximate the function using a Gaussian process.
In other words the function values are assumed to follow a multivariate
gaussian. The covariance of the function values are given by a
GP kernel between the parameters. Then a smart choice to choose the
next parameter to evaluate can be made by the acquisition function
over the Gaussian prior which is much quicker to evaluate.
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