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  1. Article ; Online: Contrast trees and distribution boosting.

    Friedman, Jerome H

    Proceedings of the National Academy of Sciences of the United States of America

    2020  Volume 117, Issue 35, Page(s) 21175–21184

    Abstract: A method for decision tree induction is presented. Given a set of predictor variables [Formula: see text] and two outcome variables y and z associated with each x, the goal is to identify those values of x for which the respective distributions of [ ... ...

    Abstract A method for decision tree induction is presented. Given a set of predictor variables [Formula: see text] and two outcome variables y and z associated with each x, the goal is to identify those values of x for which the respective distributions of [Formula: see text] and [Formula: see text], or selected properties of those distributions such as means or quantiles, are most different. Contrast trees provide a lack-of-fit measure for statistical models of such statistics, or for the complete conditional distribution [Formula: see text], as a function of x. They are easily interpreted and can be used as diagnostic tools to reveal and then understand the inaccuracies of models produced by any learning method. A corresponding contrast-boosting strategy is described for remedying any uncovered errors, thereby producing potentially more accurate predictions. This leads to a distribution-boosting strategy for directly estimating the full conditional distribution of y at each x under no assumptions concerning its shape, form, or parametric representation.
    Language English
    Publishing date 2020-08-19
    Publishing country United States
    Document type Journal Article
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.1921562117
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Contrast Trees and Distribution Boosting

    Friedman, Jerome H.

    2019  

    Abstract: Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to be ... ...

    Abstract Often machine learning methods are applied and results reported in cases where there is little to no information concerning accuracy of the output. Simply because a computer program returns a result does not insure its validity. If decisions are to be made based on such results it is important to have some notion of their veracity. Contrast trees represent a new approach for assessing the accuracy of many types of machine learning estimates that are not amenable to standard (cross) validation methods. In situations where inaccuracies are detected boosted contrast trees can often improve performance. A special case, distribution boosting, provides an assumption free method for estimating the full probability distribution of an outcome variable given any set of joint input predictor variable values.

    Comment: 18 pages, 20 figures
    Keywords Statistics - Machine Learning ; Computer Science - Machine Learning
    Publishing date 2019-12-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article: A Pliable Lasso.

    Tibshirani, Robert / Friedman, Jerome

    Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America

    2020  Volume 29, Issue 1, Page(s) 215–225

    Abstract: We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of some prespecified modifying variables. These modifiers might be variables such as gender, age, or time. The paradigm is quite general, ... ...

    Abstract We propose a generalization of the lasso that allows the model coefficients to vary as a function of a general set of some prespecified modifying variables. These modifiers might be variables such as gender, age, or time. The paradigm is quite general, with each lasso coefficient modified by a sparse linear function of the modifying variables
    Language English
    Publishing date 2020-09-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2014382-5
    ISSN 1537-2715 ; 1061-8600
    ISSN (online) 1537-2715
    ISSN 1061-8600
    DOI 10.1080/10618600.2019.1648271
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Representational Gradient Boosting: Backpropagation in the Space of Functions.

    Valdes, Gilmer / Friedman, Jerome H / Jiang, Fei / Gennatas, Efstathios D

    IEEE transactions on pattern analysis and machine intelligence

    2022  Volume 44, Issue 12, Page(s) 10186–10195

    Abstract: The estimation of nested functions (i.e., functions of functions) is one of the central reasons for the success and popularity of machine learning. Today, artificial neural networks are the predominant class of algorithms in this area, known as ... ...

    Abstract The estimation of nested functions (i.e., functions of functions) is one of the central reasons for the success and popularity of machine learning. Today, artificial neural networks are the predominant class of algorithms in this area, known as representational learning. Here, we introduce Representational Gradient Boosting (RGB), a nonparametric algorithm that estimates functions with multi-layer architectures obtained using backpropagation in the space of functions. RGB does not need to assume a functional form in the nodes or output (e.g., linear models or rectified linear units), but rather estimates these transformations. RGB can be seen as an optimized stacking procedure where a meta algorithm learns how to combine different classes of functions (e.g., Neural Networks (NN) and Gradient Boosting (GB)), while building and optimizing them jointly in an attempt to compensate each other's weaknesses. This highlights a stark difference with current approaches to meta-learning that combine models only after they have been built independently. We showed that providing optimized stacking is one of the main advantages of RGB over current approaches. Additionally, due to the nested nature of RGB we also showed how it improves over GB in problems that have several high-order interactions. Finally, we investigate both theoretically and in practice the problem of recovering nested functions and the value of prior knowledge.
    MeSH term(s) Algorithms ; Neural Networks, Computer ; Machine Learning
    Language English
    Publishing date 2022-11-07
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ISSN 1939-3539
    ISSN (online) 1939-3539
    DOI 10.1109/TPAMI.2021.3137715
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron

    Friedman, Jerome / Hastie, Trevor / Tibshirani, Robert

    Journal of the American Statistical Association. 2020 Apr. 2, v. 115, no. 530 p.665-666

    2020  

    Abstract: Professor Efron has presented us with a thought-provoking paper on the relationship between prediction, estimation, and attribution in the modern era of data science. While we appreciate many of his arguments, we see more of a continuum between the old ... ...

    Abstract Professor Efron has presented us with a thought-provoking paper on the relationship between prediction, estimation, and attribution in the modern era of data science. While we appreciate many of his arguments, we see more of a continuum between the old and new methodology, and the opportunity for both to improve through their synergy.
    Keywords Americans ; journals ; methodology ; prediction
    Language English
    Dates of publication 2020-0402
    Size p. 665-666.
    Publishing place Taylor & Francis
    Document type Article ; Online
    ZDB-ID 2064981-2
    ISSN 1537-274X
    ISSN 1537-274X
    DOI 10.1080/01621459.2020.1762617
    Database NAL-Catalogue (AGRICOLA)

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  6. Book ; Online: Lockout

    Valdes, Gilmer / Arbelo, Wilmer / Interian, Yannet / Friedman, Jerome H.

    Sparse Regularization of Neural Networks

    2021  

    Abstract: Many regression and classification procedures fit a parameterized function $f(x;w)$ of predictor variables $x$ to data $\{x_{i},y_{i}\}_1^N$ based on some loss criterion $L(y,f)$. Often, regularization is applied to improve accuracy by placing a ... ...

    Abstract Many regression and classification procedures fit a parameterized function $f(x;w)$ of predictor variables $x$ to data $\{x_{i},y_{i}\}_1^N$ based on some loss criterion $L(y,f)$. Often, regularization is applied to improve accuracy by placing a constraint $P(w)\leq t$ on the values of the parameters $w$. Although efficient methods exist for finding solutions to these constrained optimization problems for all values of $t\geq0$ in the special case when $f$ is a linear function, none are available when $f$ is non-linear (e.g. Neural Networks). Here we present a fast algorithm that provides all such solutions for any differentiable function $f$ and loss $L$, and any constraint $P$ that is an increasing monotone function of the absolute value of each parameter. Applications involving sparsity inducing regularization of arbitrary Neural Networks are discussed. Empirical results indicate that these sparse solutions are usually superior to their dense counterparts in both accuracy and interpretability. This improvement in accuracy can often make Neural Networks competitive with, and sometimes superior to, state-of-the-art methods in the analysis of tabular data.
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Subject code 519 ; 006
    Publishing date 2021-07-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article: Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent.

    Simon, Noah / Friedman, Jerome / Hastie, Trevor / Tibshirani, Rob

    Journal of statistical software

    2016  Volume 39, Issue 5, Page(s) 1–13

    Abstract: We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of ... ...

    Abstract We introduce a pathwise algorithm for the Cox proportional hazards model, regularized by convex combinations of ℓ
    Language English
    Publishing date 2016-03-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2010240-9
    ISSN 1548-7660
    ISSN 1548-7660
    DOI 10.18637/jss.v039.i05
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book: The elements of statistical learning

    Hastie, Trevor / Friedman, Jerome H / Tibshirani, Robert

    data mining, inference, and prediction

    (Springer series in statistics)

    2013  

    Author's details Trevor Hastie; Robert Tibshirani; Jerome Friedman
    Series title Springer series in statistics
    Keywords Maschinelles Lernen ; Statistik
    Language English
    Size XXII, 745 S., Ill., graph. Darst.
    Edition 2. ed., corr. at 7. printing
    Document type Book
    Note Literaturverz. S. [699] - 727
    ISBN 9780387848570 ; 9780387848587 ; 0387848576 ; 0387848584
    Database Federal Institute for Risk Assessment

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  9. Article ; Online: Reply to Nock and Nielsen: On the work of Nock and Nielsen and its relationship to the additive tree.

    Valdes, Gilmer / Luna, José Marcio / Gennatas, Efstathios D / Ungar, Lyle H / Eaton, Eric / Diffenderfer, Eric S / Jensen, Shane T / Simone, Charles B / Friedman, Jerome H / Solberg, Timothy D

    Proceedings of the National Academy of Sciences of the United States of America

    2020  Volume 117, Issue 16, Page(s) 8694–8695

    MeSH term(s) Decision Trees
    Language English
    Publishing date 2020-04-07
    Publishing country United States
    Document type Letter ; Comment
    ZDB-ID 209104-5
    ISSN 1091-6490 ; 0027-8424
    ISSN (online) 1091-6490
    ISSN 0027-8424
    DOI 10.1073/pnas.2002399117
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  10. Article: SparseNet

    Mazumder, Rahul / Friedman, Jerome H / Hastie, Trevor

    Journal of the American Statistical Association

    2011  Volume 106, Issue 495, Page(s) 1125–1138

    Abstract: We address the problem of sparse selection in linear models. A number of nonconvex penalties have been proposed in the literature for this purpose, along with a variety of convex-relaxation algorithms for finding good solutions. In this article we pursue ...

    Abstract We address the problem of sparse selection in linear models. A number of nonconvex penalties have been proposed in the literature for this purpose, along with a variety of convex-relaxation algorithms for finding good solutions. In this article we pursue a coordinate-descent approach for optimization, and study its convergence properties. We characterize the properties of penalties suitable for this approach, study their corresponding threshold functions, and describe a
    Language English
    Publishing date 2011
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2064981-2
    ISSN 1537-274X ; 0162-1459 ; 0003-1291
    ISSN (online) 1537-274X
    ISSN 0162-1459 ; 0003-1291
    DOI 10.1198/jasa.2011.tm09738
    Database MEDical Literature Analysis and Retrieval System OnLINE

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