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Many sciences have made significant breakthroughs by adopting online tools that help organize, structure and mine information that is too detailed to be printed in journals. In this paper, we introduce OpenML, a place for machine learning researchers to share and organize data in fine detail, so that they can work more effectively, be more visible, and(More)
Identifying the best machine learning algorithm for a given problem continues to be an active area of research. In this paper we present a new method which exploits both meta-level information acquired in past experiments and active testing, an algorithm selection strategy. Active testing attempts to iteratively identify an algorithm whose performance will(More)
We present OpenML, a novel open science platform that provides easy access to machine learning data, software and results to encourage further study and application. It organizes all submitted results online so they can be easily found and reused, and features a web API which is being integrated in popular machine learning tools such as Weka, KNIME,(More)
We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier performs best on the entire stream. This yields promising results and interesting patterns. In a second experiment, we build a(More)
Non-deterministic Constraint Logic is a family of graph games introduced by Demaine and Hearn that facilitates the construction of complexity proofs. It is convenient for the analysis of games, providing a uniform view. We focus on the acyclic version, apply this to Klondike, Mahjong Solitaire and Nonogram (that requires planarity), and discuss the more(More)
Dou Shou Qi is a game in which two players control a number of pieces, each of them aiming to move one of their pieces onto a given square. We implemented an engine for analyzing the game. Moreover, we created a series of endgame tablebases containing all configurations with up to four pieces. These tablebases are the first steps towards theoretically(More)
OpenML is an online platform where scientists can automatically log and share machine learning data sets, code, and experiments, organize them online, and build directly on the work of others. It helps to automate many tedious aspects of research, is readily integrated into several machine learning tools, and offers easy-to-use APIs. It also enables(More)
OpenML is an online, collaborative environment for machine learning where researchers and practitioners can share datasets, work-flows and experiments. While it is integrated in several machine learning environments, it was not yet integrated into environments that offer a graphical interface to easily build and experiment with many data analysis workflows.(More)