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scikit-learn is an increasingly popular machine learning library. Written in Python, it is designed to be simple and efficient, accessible to non-experts, and reusable in various contexts. In this paper, we present and discuss our design choices for the application programming interface (API) of the project. In particular, we describe the simple and elegant(More)
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data. The proposed algorithm consists of two steps. First, processing the raw signals to detect neural peak activities. Second, inferring the degree of association between neurons from partial correlation statistics. This paper summarises the(More)
The goal of this one page abstract is to present the following article (Joly et al., 2012). High-dimensional supervised learning problems, e.g. in image exploitation and bioinformatics, are more frequent than ever. Tree-based ensemble methods, such as random forests (Breiman, 2001) and extremely ran-domized trees (Geurts et al., 2006), are effective(More)
Given a set of n samples of input-output pairs ((x i , y i) ∈ (X × Y)) n i=1 , a supervised learning task is defined as searching for the function f : X → Y in a hypothesis space that minimizes some loss function over the joint distribution of input-output pairs. In multi-label classification, y i is a subset of the label space Y of size p.
Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior. Decision trees characterize the input-output relationship through a series of nested $if-then-else$ questions, the testing nodes, leading to a set of predictions, the leaf nodes. Several of such trees are(More)
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