�ݺ�ߣshows by User: glouppe

�ݺ�ߣshows by User: glouppe / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: glouppe / Wed, 08 Apr 2015 07:23:32 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: glouppe Tree models with Scikit-Learn: Great models with little assumptions /slideshow/slides-46767187/46767187 slides-150408072332-conversion-gate01
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.]]>
This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.]]> Wed, 08 Apr 2015 07:23:32 GMT /slideshow/slides-46767187/46767187 glouppe@slideshare.net(glouppe) Tree models with Scikit-Learn: Great models with little assumptions glouppe This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-150408072332-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> This talk gives an introduction to tree-based methods, both from a theoretical and practical point of view. It covers decision trees, random forests and boosting estimators, along with concrete examples based on Scikit-Learn about how they work, when they work and why they work.

Tree models with Scikit-Learn: Great models with little assumptions from Gilles Louppe

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0 Bias-variance decomposition in Random Forests /slideshow/slides2-42551950/42551950 slides2-141210012554-conversion-gate02
A brief introduction on the bias-variance decomposition in random forests]]>
A brief introduction on the bias-variance decomposition in random forests]]> Wed, 10 Dec 2014 01:25:54 GMT /slideshow/slides2-42551950/42551950 glouppe@slideshare.net(glouppe) Bias-variance decomposition in Random Forests glouppe A brief introduction on the bias-variance decomposition in random forests <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides2-141210012554-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> A brief introduction on the bias-variance decomposition in random forests

Bias-variance decomposition in Random Forests from Gilles Louppe

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0 Scikit-Learn in Particle Physics /slideshow/scikitlearn-in-particle-physics/42527760 slides-141209105556-conversion-gate01
Scikit-Learn in Particle Physics]]>
Scikit-Learn in Particle Physics]]> Tue, 09 Dec 2014 10:55:56 GMT /slideshow/scikitlearn-in-particle-physics/42527760 glouppe@slideshare.net(glouppe) Scikit-Learn in Particle Physics glouppe Scikit-Learn in Particle Physics <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-141209105556-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Scikit-Learn in Particle Physics

Scikit-Learn in Particle Physics from Gilles Louppe

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0 Understanding Random Forests: From Theory to Practice /slideshow/understanding-random-forests-from-theory-to-practice/40104506 slides-141010042109-conversion-gate01
�ݺ�ߣs of my PhD defense, held on October 9.]]>
�ݺ�ߣs of my PhD defense, held on October 9.]]> Fri, 10 Oct 2014 04:21:09 GMT /slideshow/understanding-random-forests-from-theory-to-practice/40104506 glouppe@slideshare.net(glouppe) Understanding Random Forests: From Theory to Practice glouppe �ݺ�ߣs of my PhD defense, held on October 9. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-141010042109-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs of my PhD defense, held on October 9.

Understanding Random Forests: From Theory to Practice from Gilles Louppe

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0 Accelerating Random Forests in Scikit-Learn /slideshow/accelerating-random-forests-in-scikitlearn/38492494 slides-140829101225-phpapp01
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include: - An efficient formulation of the decision tree algorithm, tailored for Random Forests; - Cythonization of the tree induction algorithm; - CPU cache optimizations, through low-level organization of data into contiguous memory blocks; - Efficient multi-threading through GIL-free routines; - A dedicated sorting procedure, taking into account the properties of data; - Shared pre-computations whenever critical. Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.]]>
Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include: - An efficient formulation of the decision tree algorithm, tailored for Random Forests; - Cythonization of the tree induction algorithm; - CPU cache optimizations, through low-level organization of data into contiguous memory blocks; - Efficient multi-threading through GIL-free routines; - A dedicated sorting procedure, taking into account the properties of data; - Shared pre-computations whenever critical. Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.]]> Fri, 29 Aug 2014 10:12:25 GMT /slideshow/accelerating-random-forests-in-scikitlearn/38492494 glouppe@slideshare.net(glouppe) Accelerating Random Forests in Scikit-Learn glouppe Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include: - An efficient formulation of the decision tree algorithm, tailored for Random Forests; - Cythonization of the tree induction algorithm; - CPU cache optimizations, through low-level organization of data into contiguous memory blocks; - Efficient multi-threading through GIL-free routines; - A dedicated sorting procedure, taking into account the properties of data; - Shared pre-computations whenever critical. Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-140829101225-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Random Forests are without contest one of the most robust, accurate and versatile tools for solving machine learning tasks. Implementing this algorithm properly and efficiently remains however a challenging task involving issues that are easily overlooked if not considered with care. In this talk, we present the Random Forests implementation developed within the Scikit-Learn machine learning library. In particular, we describe the iterative team efforts that led us to gradually improve our codebase and eventually make Scikit-Learn's Random Forests one of the most efficient implementations in the scientific ecosystem, across all libraries and programming languages. Algorithmic and technical optimizations that have made this possible include: - An efficient formulation of the decision tree algorithm, tailored for Random Forests; - Cythonization of the tree induction algorithm; - CPU cache optimizations, through low-level organization of data into contiguous memory blocks; - Efficient multi-threading through GIL-free routines; - A dedicated sorting procedure, taking into account the properties of data; - Shared pre-computations whenever critical. Overall, we believe that lessons learned from this case study extend to a broad range of scientific applications and may be of interest to anybody doing data analysis in Python.

Accelerating Random Forests in Scikit-Learn from Gilles Louppe

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0 Understanding variable importances in forests of randomized trees /slideshow/understanding-variable-importances-in-forests-of-randomized-trees/35514565 slides-140605024501-phpapp01
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]]> Thu, 05 Jun 2014 02:45:01 GMT /slideshow/understanding-variable-importances-in-forests-of-randomized-trees/35514565 glouppe@slideshare.net(glouppe) Understanding variable importances in forests of randomized trees glouppe <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-140605024501-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds" />

Understanding variable importances in forests of randomized trees from Gilles Louppe

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0 Scikit-Learn - Or why I joined an open source software project /slideshow/scikitlearn-or-why-i-joined-an-open-source-software-project/27735361 slides-131030080641-phpapp02
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]]> Wed, 30 Oct 2013 08:06:41 GMT /slideshow/scikitlearn-or-why-i-joined-an-open-source-software-project/27735361 glouppe@slideshare.net(glouppe) Scikit-Learn - Or why I joined an open source software project glouppe <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-131030080641-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds" />

Scikit-Learn - Or why I joined an open source software project from Gilles Louppe

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0 https://cdn.slidesharecdn.com/profile-photo-glouppe-48x48.jpg?cb=1523678273 Researcher in machine learning. My far ambition is to make a dent in science through data. Specialties: Machine learning, Scientific computing, Software engineering. http://www.montefiore.ulg.ac.be/~glouppe https://cdn.slidesharecdn.com/ss_thumbnails/slides-150408072332-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/slides-46767187/46767187 Tree models with Sciki... https://cdn.slidesharecdn.com/ss_thumbnails/slides2-141210012554-conversion-gate02-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/slides2-42551950/42551950 Bias-variance decompos... https://cdn.slidesharecdn.com/ss_thumbnails/slides-141209105556-conversion-gate01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/scikitlearn-in-particle-physics/42527760 Scikit-Learn in Partic...