From owner-svn-ports-all@freebsd.org Tue Nov 28 17:08:46 2017 Return-Path: Delivered-To: svn-ports-all@mailman.ysv.freebsd.org Received: from mx1.freebsd.org (mx1.freebsd.org [IPv6:2001:1900:2254:206a::19:1]) by mailman.ysv.freebsd.org (Postfix) with ESMTP id ED19EDE766F; Tue, 28 Nov 2017 17:08:46 +0000 (UTC) (envelope-from dvl@FreeBSD.org) Received: from repo.freebsd.org (repo.freebsd.org [IPv6:2610:1c1:1:6068::e6a:0]) (using TLSv1.2 with cipher ECDHE-RSA-AES256-GCM-SHA384 (256/256 bits)) (Client did not present a certificate) by mx1.freebsd.org (Postfix) with ESMTPS id B9E246EF88; Tue, 28 Nov 2017 17:08:46 +0000 (UTC) (envelope-from dvl@FreeBSD.org) Received: from repo.freebsd.org ([127.0.1.37]) by repo.freebsd.org (8.15.2/8.15.2) with ESMTP id vASH8jMg078918; Tue, 28 Nov 2017 17:08:45 GMT (envelope-from dvl@FreeBSD.org) Received: (from dvl@localhost) by repo.freebsd.org (8.15.2/8.15.2/Submit) id vASH8j28078916; Tue, 28 Nov 2017 17:08:45 GMT (envelope-from dvl@FreeBSD.org) Message-Id: <201711281708.vASH8j28078916@repo.freebsd.org> X-Authentication-Warning: repo.freebsd.org: dvl set sender to dvl@FreeBSD.org using -f From: Dan Langille Date: Tue, 28 Nov 2017 17:08:45 +0000 (UTC) To: ports-committers@freebsd.org, svn-ports-all@freebsd.org, svn-ports-head@freebsd.org Subject: svn commit: r455065 - head/devel/py-naiveBayesClassifier X-SVN-Group: ports-head X-SVN-Commit-Author: dvl X-SVN-Commit-Paths: head/devel/py-naiveBayesClassifier X-SVN-Commit-Revision: 455065 X-SVN-Commit-Repository: ports MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit X-BeenThere: svn-ports-all@freebsd.org X-Mailman-Version: 2.1.25 Precedence: list List-Id: SVN commit messages for the ports tree List-Unsubscribe: , List-Archive: List-Post: List-Help: List-Subscribe: , X-List-Received-Date: Tue, 28 Nov 2017 17:08:47 -0000 Author: dvl Date: Tue Nov 28 17:08:45 2017 New Revision: 455065 URL: https://svnweb.freebsd.org/changeset/ports/455065 Log: Provide a better description of what this is. Reported by: Alexy Dokuchaev & Adam Weinberger Modified: head/devel/py-naiveBayesClassifier/Makefile head/devel/py-naiveBayesClassifier/pkg-descr Modified: head/devel/py-naiveBayesClassifier/Makefile ============================================================================== --- head/devel/py-naiveBayesClassifier/Makefile Tue Nov 28 16:57:42 2017 (r455064) +++ head/devel/py-naiveBayesClassifier/Makefile Tue Nov 28 17:08:45 2017 (r455065) @@ -3,6 +3,7 @@ PORTNAME= naiveBayesClassifier PORTVERSION= 0.1.3 +PORTREVISION= 1 CATEGORIES= devel python MASTER_SITES= CHEESESHOP PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} Modified: head/devel/py-naiveBayesClassifier/pkg-descr ============================================================================== --- head/devel/py-naiveBayesClassifier/pkg-descr Tue Nov 28 16:57:42 2017 (r455064) +++ head/devel/py-naiveBayesClassifier/pkg-descr Tue Nov 28 17:08:45 2017 (r455065) @@ -1,4 +1,21 @@ -yet another general purpose Naive Bayesian classifier. +Yet another general purpose Naive Bayesian classifier. (under heavy development) + +Naive Bayes Classifier is probably the most widely used text classifier, +it's a supervised learning algorithm. It can be used to classify blog posts +or news articles into different categories like sports, entertainment and +so forth. + +Naive Bayes is a simple technique for constructing classifiers: models that +assign class labels to problem instances, represented as vectors of feature +values, where the class labels are drawn from some finite set. It is not a +single algorithm for training such classifiers, but a family of algorithms +based on a common principle: all naive Bayes classifiers assume that the value +of a particular feature is independent of the value of any other feature, +given the class variable. For example, a fruit may be considered to be an apple +if it is red, round, and about 10 cm in diameter. A naive Bayes classifier +considers each of these features to contribute independently to the probability +that this fruit is an apple, regardless of any possible correlations between +the color, roundness, and diameter features. WWW: https://pypi.python.org/pypi/naiveBayesClassifier