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design for machine learning software:

Abstract—Hyperopt-sklearn is a new software project that provides automatic algorithm configuration of the Scikit-learn machine learning library. Following Auto-Weka, we take the view that the choice of classifier and even the choice of preprocessing module can be taken together to represent a

API design for machine learning software: experiences from the scikit-learn project

The scikit-learn12 project [4] is an increasingly pop-ular machine learning library written in Python. It is designed to be simple and efficient, useful to both experts and non-experts, and reusable in a variety of contexts. The primary aim of the project is to provide a compendium of efficient

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease

of assessing the impact of different therapeutic strategies. These data are by necessity high dimensional and complex, driving the widespread application of machine learning techniques for their analysis. Machine learning in this context works by learning a regressor from the space of brain recordings (e

Abstract In the last years Python has gained more and more traction in the scientific community. Projects like NumPy, SciPy, and Matplotlib have created a strong foun-dation for scientific computing in Python and machine lear-ning packages like scikit-learn or packages for data analysis like Pandas

promising results where methods from machine learning (such as Support Vector Machines) have been used to identify AD-related patterns from images, for use in diagnosing new individual subjects. In this paper, we propose a new framework for AD classification which makes use of Linear Program (LP) boosting