search for: decisively

Hello, this question is a bit out of the blue. I am a big R fan and user and in my new job I do some decision modeling (mostly health economics). For that decision trees are often used (I guess the most classic example is the investment decision A, B, and C with different probabilities, what is the expected payoff). We use a specialized software called TreeAge that some might know. The basic

A client has inquired about producing a decision tree from data which could include: - ID of brand purchased - Importance ratings (1-10 scale) for a number of relevant attributes (price, strength, recommended by a friend, etc.) In other words, a rating of how important each attribute is in the decision as to which brand to purchase. I've just run a test decision tree using the closest thing

Ok is there a way to do it with decision tree? I just need to make the decision rules. Perhaps I can pick one of the trees used with Random Forrest. I am somewhat familiar already with Random Forrest with respective to bagging and feature sampling and getting the mode from the leaf nodes and it being an ensemble technique of many trees. I am just working from the perspective that I need

On 8 September 2016 at 19:10, Chris Bieneman <cbieneman at apple.com> wrote: > My personal preference would be for the decision makers to be either a > committee of developers or the LLVM Foundation board, and I would prefer if > the survey were crafted to provide them with information to inform a > decision, rather than a dictation of a decision. Hi Chris, Those are very good

Hal, Thank you for finding interest in our work! Please find my answers inlined below. > > ________________________________________ > From: Hal Finkel [hfinkel at anl.gov] > Sent: Monday, December 16, 2013 4:26 PM > To: Radovan Obradovic > Cc: llvmdev at cs.uiuc.edu; chandlerc; Andrew Trick > Subject: Re: [LLVMdev] [RFC] Iterrative compilation framework for Clang/LLVM >

This patch: * adds a C-based security policy translation tool to Xen (secpol_xml2bin) and removes the current Java security policy translator (Java dependencies). The C-based tool integrates into the Xen source tree build and install (using gnome libxml2 for XML parsing). See install.txt. * introduces security labels and related tools. Users can now use semantic-rich label names to put

This is a first step towards to full iterative compilation framework for Clang/LLVM. The attached patch is work in progress and the idea of sending it to the list earlier rather than later is to open up a discussion on iterative compilation in Clang/LLVM. All comments are welcome, especially those related to integration of the framework into Clang/LLVM. Current compilers have pipeline structure

On 2015-02-10, Always Learning <centos at u64.u22.net> wrote: > > My decisions are based on what I know. Those decisions can be called > "informed decisions". Calling them "informed decisions" doesn't automatically make them informed decisions. --keith -- kkeller at wombat.san-francisco.ca.us

Hi Everyone, Numerous people have been bringing up challenges with consensus driven decision making in the LLVM community. After considering this and seeing the frustrations it is causing many people, I think we should make a formal process change to help improve decision making going forward. Here is the outline of the draft proposal

Hi Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

Hi I'm trying to get the top decision rules from a decision tree. Eventually I will like to do this with R and Random Forrest. There has to be a way to output the decsion rules of each leaf node in an easily readable way. I am looking at the randomforrest and rpart packages and I dont see anything yet. Mike [[alternative HTML version deleted]]

Renato Golin via llvm-dev <llvm-dev at lists.llvm.org> writes: > If we want to go for meritocracy, which I think most people would > agree, then it would have to be people that already represent the > community, internally or externally. Given your close attention to representation I know that you have all the best intentions here, so please take this in the spirit of productive

Hi, I am somewhat new to R so this question may be foolish, but is it possible to add decision trees into a list, array or vector in R? I am trying to build a collection (ensemble) of decision trees. Every time a new instance arrive I need to get the prediction of each decision tree. I have tried to add a decision tree into a variable but without luck. Is a special package needed perhaps? This

Radovan, Thanks for posting this! I would really like to have this kind of functionality available. There are a lot of things to think through here; comments inline... ----- Original Message ----- > From: "Radovan Obradovic" <Radovan.Obradovic at imgtec.com> > To: llvmdev at cs.uiuc.edu > Sent: Monday, December 16, 2013 11:31:21 AM > Subject: [LLVMdev] [RFC]

Hi Radovan, I am also interested in the iterative compilation in LLVM, and had implemented a simple iterative compilation driver. I guess you do not need to embedded the pointer to ModuleDecisionTreeProxies into the core classes of llvm, i.e. the PassManager class and the Function class. Instead, you could: 1. Implement a special pass manager that runs a set of passes iteratively. Implementing

On Tue, February 10, 2015 4:04 pm, Warren Young wrote: >> On Feb 9, 2015, at 12:12 PM, John R Pierce <pierce at hogranch.com> wrote: >> >> On 2/9/2015 11:06 AM, Always Learning wrote: >>> The third item was a 16.1 MB PDF of 1,344 pages. A quick scan of the >>> PDF >>> shows every page appears to be readable. 11 pages devoted to BASH. >>>

Dear mailinglist members, I have the following problem: I run a decision tree using the rpart function and, afterwords, I try to find to which leaf node a new register (not used to build the decision tree) belongs to. I will try to explain better: rpart.tree <- rpart(target.value ~., data) leaf.node <- new.function(rpart.tree, new.register) The new register has all the explanatory values

Have anybody used Decision Tree in Python or C++? (or written their own decision tree implementation in Python or C++)? My goal is to run decision tree on 8 million obs as training set and score 7 million in test set. I am testing 'rpart' package on a 64-bit-Linux + 64-bit-R environment. But it seems that rpart is either not stable or running out of memory very quickly. (Is it

Nope. Random forests are not decision trees -- they are ensembles (forests) of trees. You need to go back and read up on them so you understand how they work. The Hastie/Tibshirani/Friedman "The Elements of Statistical Learning" has a nice explanation, but I'm sure there are lots of good web resources, too. Cheers, Bert Bert Gunter "The trouble with having an open mind is

Hi all, Below is a proposal for refining the way we communicate between the ThinLTO link step (the combined indexing step) and the backend processes that do the actual importing and other summary-based optimizations in a distributed backend process. Mehdi, let me know if this addresses your concerns. Peter, PTAL from the standpoint of any summary extensions needed for CFI and make sure they can