Re: XGBoost Installation

I am also using SVM, though it never completes in running time. I have
increased the cache size to 1000 however that has little effect.
Are there parameters I can set such that SVM will run in under an hour.

Kind regards,
Prakash

On Sun, Jun 6, 2021 at 10:27 AM Prakash Bhagat <[email protected]>
wrote:

> Thanks,
> I did so however in my problem the cost of a False Positive is 5 times
> more than a False Negative thus the confusion matrix used is [0.0 1.0; 5.0
> 0.0]. When implementing the setting you suggested I get an accuracy of 57%
> still relatively low.
> The highest I was able to get was 67% when using nrounds=1000.
> Could it just be that XGBoost does not work as well as knn and rf for this
> case or am I missing something?
>
> Kind regards,
> Prakash
>
> On Sun, Jun 6, 2021 at 2:13 AM Eibe Frank <[email protected]>
> wrote:
>
>> Looking at the configuration string I posted, you will see
>>
>> -learner classif.xgboost -params
>> nrounds=100,max_depth=3,subsample=0.5,colsample_bynode=0.5
>>
>> The
>>
>> -params
>>
>> flag introduces the parameters that will be used for the MLR learner that
>> is specified. You need to paste those into the corresponding text field in
>> the GenericObjectEditor when you configure MLRClassifier.
>>
>> Yes, you should use the same cost matrix.
>>
>> Cheers,
>> Eibe
>>
>> On Sun, Jun 6, 2021 at 1:09 PM Prakash Bhagat <
>> [email protected]> wrote:
>>
>>> 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
>>>>>>>>> _______________________________________________
>>>>>>>>> Wekalist mailing list -- [email protected]
>>>>>>>>> Send posts to [email protected]
>>>>>>>>> To unsubscribe send an email to [email protected]
>>>>>>>>> To subscribe, unsubscribe, etc., visit
>>>>>>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>>>>>>> List etiquette:
>>>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> Wekalist mailing list -- [email protected]
>>>>>>>> Send posts to [email protected]
>>>>>>>> To unsubscribe send an email to [email protected]
>>>>>>>> To subscribe, unsubscribe, etc., visit
>>>>>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>>>>>> List etiquette:
>>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>>>>>
>>>>>>>>
>>>>>>>> _______________________________________________
>>>>>>>> Wekalist mailing list -- [email protected]
>>>>>>>> Send posts to [email protected]
>>>>>>>> To unsubscribe send an email to [email protected]
>>>>>>>> To subscribe, unsubscribe, etc., visit
>>>>>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>>>>>> List etiquette:
>>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> Wekalist mailing list -- [email protected]
>>>>>>> Send posts to [email protected]
>>>>>>> To unsubscribe send an email to [email protected]
>>>>>>> To subscribe, unsubscribe, etc., visit
>>>>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>>>>> List etiquette:
>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>>>>
>>>>>>>
>>>>>>> _______________________________________________
>>>>>>> Wekalist mailing list -- [email protected]
>>>>>>> Send posts to [email protected]
>>>>>>> To unsubscribe send an email to [email protected]
>>>>>>> To subscribe, unsubscribe, etc., visit
>>>>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>>>>> List etiquette:
>>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>>>>
>>>>>> _______________________________________________
>>>>>> Wekalist mailing list -- [email protected]
>>>>>> Send posts to [email protected]
>>>>>> To unsubscribe send an email to [email protected]
>>>>>> To subscribe, unsubscribe, etc., visit
>>>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>>>> List etiquette:
>>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>>>
>>>>> _______________________________________________
>>>>> Wekalist mailing list -- [email protected]
>>>>> Send posts to [email protected]
>>>>> To unsubscribe send an email to [email protected]
>>>>> To subscribe, unsubscribe, etc., visit
>>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>>> List etiquette:
>>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>>
>>>> _______________________________________________
>>>> Wekalist mailing list -- [email protected]
>>>> Send posts to [email protected]
>>>> To unsubscribe send an email to [email protected]
>>>> To subscribe, unsubscribe, etc., visit
>>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>>> List etiquette:
>>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>>
>>> _______________________________________________
>>> Wekalist mailing list -- [email protected]
>>> Send posts to [email protected]
>>> To unsubscribe send an email to [email protected]
>>> To subscribe, unsubscribe, etc., visit
>>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>>> List etiquette:
>>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>>
>> _______________________________________________
>> Wekalist mailing list -- [email protected]
>> Send posts to [email protected]
>> To unsubscribe send an email to [email protected]
>> To subscribe, unsubscribe, etc., visit
>> https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
>> List etiquette:
>> http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>
>

_______________________________________________
Wekalist mailing list -- [email protected]
Send posts to [email protected]
To unsubscribe send an email to [email protected]
To subscribe, unsubscribe, etc., visit https://list.waikato.ac.nz/postorius/lists/wekalist.list.waikato.ac.nz
List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html