Re: XGBoost Installation

Thanks so much for this!
For your first point, how do I set a minimum number of boosting iterations?
I have looked within the MLR classifier setting however see no option for
min iterations.
If I use a cost matrix in the classifier then under more options for test I
should create the same confusion matrix, correct?

Kind regards,
Prakash

On Sat, Jun 5, 2021 at 12:56 PM Eibe Frank <[email protected]> wrote:

> The default number of iterations used by XGBoost when applied through the
> RPlugin is 1. You definitely need to increase the number of boosting
> iterations. I would also switch the CostSensitiveClassifier to use the
> minimum-expected cost approach, not the default approach based on
> reweighting the training data.
>
> Of course, you should also specify the same cost matrix for evaluation
> under "More options..." in the Classify tab.
>
> The following configuration worked well in a quick try on the sonar data
> from the UCI repository:
>
> weka.classifiers.meta.CostSensitiveClassifier -cost-matrix "[0.0 5.0; 1.0
> 0.0]" -M  -W weka.classifiers.mlr.MLRClassifier -- -learner classif.xgboost
> -params nrounds=100,max_depth=3,subsample=0.5,colsample_bynode=0.5
>
> You can right-click on the text field containing the classifier
> configuration in the Classify panel and paste the above configuration into
> the Explorer (assuming you have a dataset with only two classes, it should
> work).
>
> The reason for using the minimum-expected cost mode of the
> CostSensitiveClassifier is that it does not change the training data. The
> default mode of the CostSensitiveClassifier uses instance weights in the
> training data to reflect costs. I don't know how well that works in
> conjunction with MLRClassifier.
>
> Cheers,
> Eibe
>
> On Sat, Jun 5, 2021 at 11:33 PM Prakash Bhagat <
> [email protected]> wrote:
>
>> Thanks very much,
>>
>> I have managed to get windows access and followed the tutorial on youtube
>> for downloading the rplugin. I am using xgboost as a classification method
>> and comparing it to random forests. Random forest gives an accuracy of 75%
>> whereas XGBoost gives an accuracy of only 52%. I was wondering if you could
>> help me as i may have made some errors when implementing xgboost.
>> I have attached the data used. I filled in missing values for revol_util
>> and converted nominal features to binary when using xgboost.
>> I then used the costsensitiveclassifier with a 5 to 1 cost for false
>> positives and used 5 fold cross-validation. Following this, I used
>> mlrclassifier and used classif.xgboost under Rlearner.
>> I will do hyper parameter tuning after but was wandering if i had made a
>> mistake with xgboost as the accuracy is so low.
>> Any help would be much appreciated!
>>
>> Kind regards,
>> Prakash
>>
>> On Sat, Jun 5, 2021 at 4:16 AM Eibe Frank <[email protected]> wrote:
>>
>>> You cannot set environment variables, etc., in WEKA's built-in CLI,
>>> which is really quite primitive and only pretends to be a proper OS
>>> terminal! You need to run the command in the macOS terminal. However, as I
>>> said, you should not set R_HOME explicitly unless you have installed R in a
>>> non-standard location. WEKA's RPlugin will find R on the Mac if it is
>>> installed in the standard place.
>>>
>>> Cheers,
>>> Eibe
>>>
>>> On Sat, Jun 5, 2021 at 1:43 PM Prakash Bhagat <
>>> [email protected]> wrote:
>>>
>>>> Yes, I have followed this guide:
>>>> https://riptutorial.com/weka/topic/7916/how-to-use-r-in-weka
>>>> I pasted this in the command line in WEKA: export
>>>> R_HOME=/Library/Frameworks/R.framework/Resources
>>>> java -Xss10M -Xmx4096M -cp .:weka.jar weka.gui.GUIChooser
>>>>
>>>> And this is the error from weka:
>>>>
>>>> Kind regards,
>>>> Prakash
>>>>
>>>>
>>>>
>>>> On 04 Jun,2021, at 13:52, Eibe Frank <[email protected]> wrote:
>>>>
>>>> Are you still running WEKA with R_HOME set to some value? It is
>>>> normally best to let WEKA set the value of R_HOME itself.
>>>>
>>>> If there are any error messages shown in the terminal from which you
>>>> run WEKA, it would be useful to share those.
>>>>
>>>> Cheers,
>>>> Eibe
>>>>
>>>> On Fri, Jun 4, 2021 at 11:43 PM Prakash Bhagat <
>>>> [email protected]> wrote:
>>>>
>>>>> https://github.com/SigDelta/weka-xgboost/releases - This is the link
>>>>> I used to download xgboost.
>>>>> Using this downloaded I installed xgboost as an unofficial package in
>>>>> tools. It then comes under trees however does not run.
>>>>>
>>>>> With the RPlugin the MLR classifier is unavailable to use. I have
>>>>> downloaded a new version of R and within R I also downloaded the MLR
>>>>> package. This still however does not run.
>>>>> I have installed rJava within R as well.
>>>>> <Screenshot 2021-06-04 at 11.51.31.png>
>>>>>
>>>>> On 04 Jun,2021, at 11:07, Eibe Frank <[email protected]> wrote:
>>>>>
>>>>> Which unofficial version do you mean? Both, RPlugin and wekaPython,
>>>>> are official packages, and both enable you to use XGBoost. With the
>>>>> RPlugin, you need to use XGBoost through the MLRClassifier and select
>>>>> XGBoost as the base learner (the first time you select this base learner,
>>>>> it will try to install it in R, which may take a while). Similarly, with
>>>>> wekaPython, you also need to use the corresponding ScikitLearnClassifier in
>>>>> WEKA with XGBoost as the base learner (and you need to have XGBoost
>>>>> installed in your relevant Python environment, which you may need to do
>>>>> manually).
>>>>>
>>>>> The easiest way to use XGBoost in WEKA is probably through the RPlugin
>>>>> because the only manual work you (normally) need to do is to install R.
>>>>>
>>>>> There might be an unofficial WEKA package for XGBoost out there, but
>>>>> it may not be maintained.
>>>>>
>>>>> Cheers,
>>>>> Eibe
>>>>>
>>>>>
>>>>> On Fri, Jun 4, 2021 at 4:49 PM <[email protected]> wrote:
>>>>>
>>>>>> Dear All,
>>>>>>
>>>>>> I have a mac with the latest version of R and python installed. I am
>>>>>> using WEKA for an assignment and need to use XGBoost.
>>>>>> I have tried downloading the unofficial version as a package however
>>>>>> that does not work.
>>>>>> I then tried the R Plugin version downloading RPlugin and pasting
>>>>>> this in the command line in WEKA:
>>>>>> export R_HOME=/Library/Frameworks/R.framework/Resources
>>>>>> java -Xss10M -Xmx4096M -cp .:weka.jar weka.gui.GUIChoose
>>>>>>
>>>>>> This however did not work either and R does not run.
>>>>>>
>>>>>> If anyone has a very specific step-by-step guide to getting XGBoost
>>>>>> on macOS please let me know!
>>>>>>
>>>>>> Kind regards,
>>>>>> PYB
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