Hyperopt by hyperopt. Hyperopt is a Python library for optimizing over awkward search spaces with real-valued, discrete, and conditional dimensions.
Currently two algorithms are implemented in hyperopt: Random SearchTree of Parzen Estimators (TPE) Hyperopt has been designed to accommodate Bayesian optimization algorithms based on Gaussian processes and regression trees, but these are not currently implemented. All algorithms can be run either serially, or in parallel by communicating via MongoDB. Hyperopt/hyperopt. Hyperopt: A Python library for optimizing machine learning algorithms; SciPy 2013.
Multi-armed bandit. A row of slot machines in Las Vegas.
In probability theory, the multi-armed bandit problem (sometimes called the K-[1] or N-armed bandit problem[2]) is a problem in which a gambler at a row of slot machines (sometimes known as "one-armed bandits") has to decide which machines to play, how many times to play each machine and in which order to play them.[3] When played, each machine provides a random reward from a probability distribution specific to that machine. The objective of the gambler is to maximize the sum of rewards earned through a sequence of lever pulls.[4][5] Herbert Robbins in 1952, realizing the importance of the problem, constructed convergent population selection strategies in "some aspects of the sequential design of experiments".[6]
Intro to scikit-learn (II), SciPy2013 Tutorial, Part 1 of 2. Jakevdp/sklearn_pycon2013. Machine Learning Cheat Sheet (for scikit-learn) Intro to scikit-learn (I), SciPy2013 Tutorial, Part 3 of 3. Machine learning in Python. Tutorial: scikit-learn - Machine Learning in Python with Contributor Jake VanderPlas.
Jakevdp/sklearn_scipy2013. Intro to scikit-learn (I), SciPy2013 Tutorial, Part 1 of 3.