I still need the output to match my requiremnt in my original post. With
decision rules "clusters" and probability attached to them. The examples are
sort of similar. You just provided links to general info about trees.
Sent from my Verizon, Samsung Galaxy smartphone
Original messa
On Wednesday, April 13, 2016, Michael Artz wrote:
> Tjats great that you are familiar and thanks for responding. Have you
> ever done what I am referring to? I have alteady spent time going through
> links and tutorials about decision trees and random forrests and have even
> used them both befo
Tjats great that you are familiar and thanks for responding. Have you ever
done what I am referring to? I have alteady spent time going through links
and tutorials about decision trees and random forrests and have even used
them both before.
Mike
On Apr 13, 2016 5:32 PM, "Sarah Goslee" wrote:
I
It sounds like you want classification or regression trees. rpart does
exactly what you describe.
Here's an overview:
http://www.statmethods.net/advstats/cart.html
But there are a lot of other ways to do the same thing in R, for instance:
http://www.r-bloggers.com/a-brief-tour-of-the-trees-and-fo
Ah yes I will have to use the predict function. But the predict function
will not get me there really. If I can take the example that I have a
model predicting whether or not I will play golf (this is the dependent
value), and there are three independent variables Humidity(High, Medium,
Low), Pen
I think you are missing the point of random forests. But if you just
want to predict using the forest, there is a predict() method that you
can use. Other than that, I certainly don't understand what you mean.
Maybe someone else might.
Cheers,
Bert
Bert Gunter
"The trouble with having an open m
Also that being said, just because random forest are not the same thing as
decision trees does not mean that you can't get decision rules from random
forest.
On Wed, Apr 13, 2016 at 4:11 PM, Michael Artz
wrote:
> Ok is there a way to do it with decision tree? I just need to make the
> decision
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
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 resou
To back up Ber's please have a look at
http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example
and/or http://adv-r.had.co.nz/Reproducibility.html
John Kane
Kingston ON Canada
> -Original Message-
> From: jose.ferr...@logiteng.com
> Sent: Wed, 13 Apr 2016 17
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
Dear list,
The following example of constrOptim() where the initial point is the solution
shows that the component counts is not a two-element vector as documented in
the man page.
constrOptim(c(1,1), fr, grr, ui = diag(2), ci = c(0,0))
Does anyone have the same behavior?
A possible solution
code? example data? We can only guess based on your vague post.
"PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code."
Moreover, this sounds like a statistical question, not a question about R
programmi
%=>% would have precendence ('order of operations') problems also.
A + B %=>% C
is equivalent to
A + ( B %=>% C)
and I don't think that is what you want.
as.list(quote(A + B %=>% C)) shows the first branch in the parse tree. The
following function, str.language, shows the entire parse tr
CRAN turned out to have structural issues with version numbers that are not of
the x.y.z variety (some script break). I'm trying to find time to build a 3.2.5
just to fix this up. Of course all standard procedures are broken as 3.3.0 is
now in progress, so several things now need to be done manu
Hi
Without some reproducible example you hardly get any answer.
if this works
library(ggplot2)
p <- ggplot(mtcars, aes(wt, mpg))
p + geom_point()
the problem is in your data.
If it does not, the problem is elsewhere, including broken R installation.
Cheers
Petr
> -Original Message-
>
My CRAN mirror still says this:
The latest release (Thursday 2016-03-10, Very Secure Dishes)
R-3.2.4.tar.gz, read what's new in the latest version.
Should that not be updated? Anyone who has not seen that post won't
know to look further.
On Wed, 16-Mar-2016 at 08:39PM +, Peter Dalgaard
I suppose it would work, although "=>" is rather a descriptive symbol and
less a function.
But choosing between quoting:
"A + B => C"
and a regular function:
A + B %=>% C
probably quoting is the most straightforward, as the result of the foo()
function has to be a string anyways (which is parsed by
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