Re: Wekalist Digest, Vol 171, Issue 20

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Re: Wekalist Digest, Vol 171, Issue 20

Betha Nurina Sari
Thanks you for you explaination

Pada tanggal 3 Mei 2017 05.39, <[hidden email]> menulis:
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Today's Topics:

   1. Re: equation of a decision tree model in weka (Eibe Frank)
   2. Re: Java errors - Retrieving data from instances (Eibe Frank)
   3. Re: about furia in weka (Eibe Frank)
   4. Re: Re : CorrelationAttibuteEval (Mark Hall)


----------------------------------------------------------------------

Message: 1
Date: Wed, 3 May 2017 10:25:49 +1200
From: Eibe Frank <[hidden email]>
To: "Weka machine learning workbench list."
        <[hidden email]>
Subject: Re: [Wekalist] equation of a decision tree model in weka
Message-ID: <[hidden email]>
Content-Type: text/plain; charset=utf-8

There is no simple equation to represent a decision tree. I?m not sure what you are trying to do.

Some decision tree learners in WEKA have the ability to export the classifier as Java source code, and some can also output the tree structure in dot graph description language, but those are pretty much the only export options at this stage.

You can run Java code from Matlab, so you can call all WEKA methods from Matlab, including methods that build a model and use it for prediction.

Cheers,
Eibe

> On 2/05/2017, at 11:13 PM, Dimple Shaj <[hidden email]> wrote:
>
> Hi
>
> Can anyone tell me how to find the regression equation of a decision tree model, what i mean is function approximation in weka. Is it possible directly from weka or should i export the finalized model to matlab and find it. How to export the decision tree model from weka to matlab?
> Can anyone pls help me ....
>
>
> Dimple
>
> _______________________________________________
> Wekalist mailing list
> Send posts to: [hidden email]
> List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
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------------------------------

Message: 2
Date: Wed, 3 May 2017 10:31:58 +1200
From: Eibe Frank <[hidden email]>
To: "Weka machine learning workbench list."
        <[hidden email]>
Subject: Re: [Wekalist] Java errors - Retrieving data from instances
Message-ID: <[hidden email]>
Content-Type: text/plain; charset=utf-8

It should find fcs_all if all your code is in the same Java method.

Cheers,
Eibe

> On 2/05/2017, at 9:00 PM, Bob Matthews <[hidden email]> wrote:
>
> Hi Mark
>
> now have the following code.............
>
> // get index, OHLC prices and the take profit from (l)th instance
> sim_trading_date = lthInstance.value(0);
> sim_trading_time = lthInstance.value(1);
> sim_open_price = lthInstance.value(2);
> sim_high_price = lthInstance.value(3);
> sim_low_price = lthInstance.value(4);
> sim_close_price = lthInstance.value(5);
> sim_take_profit = lthInstance.value(15);
>
> seems to be OK
>
> so my only problem is why it is not recognizing fcs_all
>
> Bob M
>
> On 5/2/17 8:13 PM, Mark Hall wrote:
>> My mail client had a fit ? the rest of my message was going to be:
>>
>> See:
>> http://weka.sourceforge.net/doc.stable-3-8/weka/core/Instance.html
>>
>> Cheers,
>> Mark.
>>
>> On 2/05/17, 7:27 PM, "Bob Matthews" <[hidden email] on behalf of [hidden email]> wrote:
>>
>>                                            Hello Eibe
>>                  I have the following code.......................
>>                  ObjectInputStream ois =
>>             new ObjectInputStream(
>>                                        new
>>             FileInputStream("C:/............/KStar1.model"));
>>             FilteredClassifier fcs_all = (FilteredClassifier)
>>             ois.readObject();
>>             ois.close();
>>                    //retrieve the lth instance in the testing set
>>         Instance lthInstance = testing.instance(l);
>>                  // get index, OHLC prices and the take profit from (l)th instance
>>         sim_trading_date = lthInstance.attribute(Trading_Date, 0);
>>         sim_trading_time = lthInstance.attribute(Trading_Time, 1);
>>         In the above 2 lines, it cannot find the
>>           variables
>>                  sim_open_price = lthInstance.attribute(open_price, 2);
>>         sim_high_price = lthInstance.attribute(high_price, 3);
>>         sim_low_price = lthInstance.attribute(low_price, 4);
>>         sim_close_price = lthInstance.attribute(close_price, 5);
>>         sim_take_profit = lthInstance.attribute(take_profit, 15);
>>         In the above 5 lines, method attribute cannot
>>           be applied
>>                  // classifyInstance() just returns the index of the predicted label
>>         (the one with the highest probability) as a double
>>         pred = fcs_all.classifyInstance(lthInstance);
>>           Cannot find variable fcs_all
>>                      Is there something obvious that I am doing
>>             wrong here?
>>                      Bob M
>>                          _______________________________________________
>>     Wekalist mailing list
>>     Send posts to: [hidden email]
>>     List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
>>     List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>>
>>
>> _______________________________________________
>> Wekalist mailing list
>> Send posts to: [hidden email]
>> List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
>> List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
>
> _______________________________________________
> Wekalist mailing list
> Send posts to: [hidden email]
> List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
> List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html



------------------------------

Message: 3
Date: Wed, 3 May 2017 10:32:42 +1200
From: Eibe Frank <[hidden email]>
To: "Weka machine learning workbench list."
        <[hidden email]>
Subject: Re: [Wekalist] about furia in weka
Message-ID: <[hidden email]>
Content-Type: text/plain; charset=us-ascii

The FURIA source code comes with the FURIA package.

Cheers,
Eibe

> On 3/05/2017, at 3:14 AM, Betha Nurina Sari <[hidden email]> wrote:
>
> Dear all,
> Please help me to explain the manual step or maybe you can give the example implementation FURIA.
>
> I need the explaination how to implement FURIA as classifier. I read the paper from Jens Huns about FURIA,but i am still confused.
>
> Maybe, you can give me more clearly step and example. I got the exercise from my lecture to explain detail from read dataset until predict the class target with furia.
>
> Thanks, hopefully any one can give the answers
>
> Regards,
> Betha
> _______________________________________________
> Wekalist mailing list
> Send posts to: [hidden email]
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------------------------------

Message: 4
Date: Wed, 03 May 2017 10:38:38 +1200
From: Mark Hall <[hidden email]>
To: "Weka machine learning workbench list."
        <[hidden email]>
Subject: Re: [Wekalist] Re : CorrelationAttibuteEval
Message-ID: <[hidden email]>
Content-Type: text/plain;       charset="UTF-8"

A nominal attribute with k values is essentially converted into k separate binary indicator attributes, where each takes the value 1 only when its corresponding nominal value appears in a given instance (and is 0 otherwise). These indicators can then be treated as numeric and Pearsons's correlation can be computed between each and the target. A weighted average of the Pearson's correlation for each indicator is then taken as the overall correlation, where the weights are proportional to the frequency of each nominal value.

Cheers,
Mark.

On 3/05/17, 3:16 AM, "Betha Nurina Sari" <[hidden email] on behalf of [hidden email]> wrote:

    Dear all,i need explaiation about CorrelationAttributeEval. In documentation,we can read this :
    CorrelationAttributeEval:
    Evaluates the worth of an attribute by measuring the correlation (Pearson's) between it and the class.

    Nominal attributes are considered on a value by value basis by treating each value as an indicator. An overall correlation for a nominal attribute is arrived at via a weighted average.
    So,the class here is nominal attribute?
    But,i am still confuse with this sentence :An overall correlation for a nominal attribute is arrived at via a weighted average. What is the meaning of weighted average, how we can calculate it from nominal attribute (target class)?

    Thanks.



    Pada tanggal 2 Mei 2017 16.02,  <[hidden email]> menulis:

    Send Wekalist mailing list submissions to
            [hidden email]

    To subscribe or unsubscribe via the World Wide Web, visit
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    or, via email, send a message with subject or body 'help' to
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    You can reach the person managing the list at
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    When replying, please edit your Subject line so it is more specific
    than "Re: Contents of Wekalist digest..."


    Today's Topics:

       1. Re: Java errors - Retrieving data from instances (Bob Matthews)
       2. Re: Predictive markers (Santosh Bhosale)
       3. Re: Predictive markers (Santosh Bhosale)


    ----------------------------------------------------------------------

    Message: 1
    Date: Tue, 2 May 2017 21:00:35 +1200
    From: Bob Matthews <[hidden email]>
    To: [hidden email]
    Subject: Re: [Wekalist] Java errors - Retrieving data from instances
    Message-ID: <[hidden email]>
    Content-Type: text/plain; charset=utf-8; format=flowed

    Hi Mark

    now have the following code.............

    // get index, OHLC prices and the take profit from (l)th instance
    sim_trading_date = lthInstance.value(0);
    sim_trading_time = lthInstance.value(1);
    sim_open_price = lthInstance.value(2);
    sim_high_price = lthInstance.value(3);
    sim_low_price = lthInstance.value(4);
    sim_close_price = lthInstance.value(5);
    sim_take_profit = lthInstance.value(15);

    seems to be OK

    so my only problem is why it is not recognizing fcs_all

    Bob M

    On 5/2/17 8:13 PM, Mark Hall wrote:
    > My mail client had a fit ? the rest of my message was going to be:
    >
    > See:
    > http://weka.sourceforge.net/doc.stable-3-8/weka/core/Instance.html
    >
    > Cheers,
    > Mark.
    >
    > On 2/05/17, 7:27 PM, "Bob Matthews" <[hidden email] on behalf of [hidden email]> wrote:
    >
    >
    >
    >
    >
    >
    >          Hello Eibe
    >
    >          I have the following code.......................
    >
    >          ObjectInputStream ois =
    >              new ObjectInputStream(
    >                                         new
    >              FileInputStream("C:/............/KStar1.model"));
    >              FilteredClassifier fcs_all = (FilteredClassifier)
    >              ois.readObject();
    >              ois.close();
    >
    >          //retrieve the lth instance in the testing set
    >          Instance lthInstance = testing.instance(l);
    >
    >          // get index, OHLC prices and the take profit from (l)th instance
    >          sim_trading_date = lthInstance.attribute(Trading_Date, 0);
    >          sim_trading_time = lthInstance.attribute(Trading_Time, 1);
    >          In the above 2 lines, it cannot find the
    >            variables
    >
    >          sim_open_price = lthInstance.attribute(open_price, 2);
    >          sim_high_price = lthInstance.attribute(high_price, 3);
    >          sim_low_price = lthInstance.attribute(low_price, 4);
    >          sim_close_price = lthInstance.attribute(close_price, 5);
    >          sim_take_profit = lthInstance.attribute(take_profit, 15);
    >          In the above 5 lines, method attribute cannot
    >            be applied
    >
    >          // classifyInstance() just returns the index of the predicted label
    >          (the one with the highest probability) as a double
    >          pred = fcs_all.classifyInstance(lthInstance);
    >            Cannot find variable fcs_all
    >
    >            Is there something obvious that I am doing
    >              wrong here?
    >
    >            Bob M
    >
    >
    >
    >      _______________________________________________
    >      Wekalist mailing list
    >      Send posts to: [hidden email]
    >      List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
    >      List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html
    >
    >
    >
    > _______________________________________________
    > Wekalist mailing list
    > Send posts to: [hidden email]
    > List info and subscription status: https://list.waikato.ac.nz/mailman/listinfo/wekalist
    > List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html



    ------------------------------

    Message: 2
    Date: Tue, 2 May 2017 12:00:59 +0300
    From: Santosh Bhosale <[hidden email]>
    To: "Weka machine learning workbench list."
            <[hidden email]>
    Subject: Re: [Wekalist] Predictive markers
    Message-ID:
            <CA+=[hidden email]>
    Content-Type: text/plain; charset="utf-8"

    Hi Eibe,

    I tried the one you said below

    "Try the AttributeSelectedClassifier with WrapperSubsetEval, choosing
    RandomForest as the evaluator in WrapperSubsetEval and also as the base
    classifier in AttributeSelectedClassifier. The default BestFirstSearch
    method will probably work fine on your data because it is quite a small
    dataset"

    The SPSS, I just used for ROC curve plotting not for classification. So the
    only concern is why such discrepancy in the AUCs. Did I make the correct
    use of WEKA? Did my CSV input ot WEKA was correct?

    Thanks
    Santosh



    On Tue, May 2, 2017 at 11:52 AM, Eibe Frank <[hidden email]> wrote:

    > Which classifier did you use in WEKA and SPSS and which evaluation method?
    > 10-fold cross-validation?
    >
    > Cheers,
    > Eibe
    >
    > On 2/05/2017, at 6:58 PM, Santosh Bhosale <[hidden email]>
    > wrote:
    >
    > Hi Eibe,
    >
    > I tried your mentioned workflow. In result, WEKA showed a panel of 13
    > attributes (Protein biomarkers) classifying cases from controls. On the
    > same data, I drew ROC curve using WEKA, which gave AUC value of 0.937. But
    > when I took the same combination and drew the ROC curve using SPSS, it was
    > giving me AUC of 0.73. I am not understanding this discrepancy.
    >
    > Please see attached example of CSV file used as input for WEKA.
    >
    > Thanks in advance
    > Santosh
    >
    > On Wed, Apr 26, 2017 at 12:18 PM, Eibe Frank <[hidden email]> wrote:
    >
    >> Try the AttributeSelectedClassifier with WrapperSubsetEval, choosing
    >> RandomForest as the evaluator in WrapperSubsetEval and also as the base
    >> classifier in AttributeSelectedClassifier. The default BestFirstSearch
    >> method will probably work fine on your data because it is quite a small
    >> dataset.
    >>
    >> Cheers,
    >> Eibe
    >>
    >> > On 26 Apr 2017, at 00:39, Santosh Bhosale <[hidden email]>
    >> wrote:
    >> >
    >> > Dear All,
    >> >
    >> > I did following steps in WEKA.
    >> >
    >> > - Uploaded the data in CSV file format
    >> > - Ran classifier using J48 and RandomForest
    >> > - J48 gave about 77% correctly classified instances
    >> > - RandomForest gave about 84% correctly classified instances
    >> >
    >> > I had 264 instances and 24 attributes. However, I was not able to
    >> pinpoint which combination of attributes had given the best classification
    >> of cases from controls.
    >> >
    >> > Any help would be highly appreciated.
    >> >
    >> > Thanks
    >> > Santosh
    >> >
    >> > On Tue, Apr 25, 2017 at 9:52 AM, Santosh Bhosale <
    >> [hidden email]> wrote:
    >> > Hi Eibe.
    >> >
    >> > Thanks
    >> >
    >> > On Tue, Apr 25, 2017 at 6:29 AM, Eibe Frank <[hidden email]> wrote:
    >> > You should probably learn about some basics of machine learning first.
    >> There are some free on-line courses based on WEKA here:
    >> >
    >> >   https://weka.waikato.ac.nz/explorer
    >> >
    >> > Cheers,
    >> > Eibe
    >> >
    >> > > On 25 Apr 2017, at 00:32, Santosh Bhosale <[hidden email]>
    >> wrote:
    >> > >
    >> > > Hi All,
    >> > >
    >> > > I am proteomics expert and new to machine learning. I have protein
    >> expression data between cases and controls where I have already found
    >> significant markers. Now I want to predict a panel of markers which will
    >> best classify cases from controls. I am not sure how to do that.
    >> > >
    >> > > It will be really good if someone urgently helps me in the context of
    >> how the data-structure to be and what sort of pipeline to follow. So using
    >> this information I can plot the ROC curve to best classify cases from
    >> controls.
    >> > >
    >> > > Thanks in advance
    >> > >
    >> > > -Santosh
    >> > > _______________________________________________
    >> > > Wekalist mailing list
    >> > > Send posts to: [hidden email]
    >> > > List info and subscription status: https://list.waikato.ac.nz/mai
    >> lman/listinfo/wekalist
    >> > > List etiquette: http://www.cs.waikato.ac.nz/~m
    >> l/weka/mailinglist_etiquette.html
    >> >
    >> > _______________________________________________
    >> > Wekalist mailing list
    >> > Send posts to: [hidden email]
    >> > List info and subscription status: https://list.waikato.ac.nz/mai
    >> lman/listinfo/wekalist
    >> > List etiquette: http://www.cs.waikato.ac.nz/~m
    >> l/weka/mailinglist_etiquette.html
    >> >
    >> >
    >> > _______________________________________________
    >> > Wekalist mailing list
    >> > Send posts to: [hidden email]
    >> > List info and subscription status: https://list.waikato.ac.nz/mai
    >> lman/listinfo/wekalist
    >> > List etiquette: http://www.cs.waikato.ac.nz/~m
    >> l/weka/mailinglist_etiquette.html
    >>
    >> _______________________________________________
    >> Wekalist mailing list
    >> Send posts to: [hidden email]
    >> List info and subscription status: https://list.waikato.ac.nz/mai
    >> lman/listinfo/wekalist
    >> List etiquette: http://www.cs.waikato.ac.nz/~m
    >> l/weka/mailinglist_etiquette.html
    >>
    >
    > <exampleOfWEKAInput.csv>
    >
    > _______________________________________________
    > Wekalist mailing list
    > Send posts to: [hidden email]
    > List info and subscription status: https://list.waikato.ac.nz/
    > mailman/listinfo/wekalist
    > List etiquette: http://www.cs.waikato.ac.nz/~
    > ml/weka/mailinglist_etiquette.html
    >
    >
    > _______________________________________________
    > Wekalist mailing list
    > Send posts to: [hidden email]
    > List info and subscription status: https://list.waikato.ac.nz/
    > mailman/listinfo/wekalist
    > List etiquette: http://www.cs.waikato.ac.nz/~
    > ml/weka/mailinglist_etiquette.html
    >
    >
    -------------- next part --------------
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    ------------------------------

    Message: 3
    Date: Tue, 2 May 2017 12:02:00 +0300
    From: Santosh Bhosale <[hidden email]>
    To: "Weka machine learning workbench list."
            <[hidden email]>
    Subject: Re: [Wekalist] Predictive markers
    Message-ID:
            <CA+=4V7US7+ZMCO8CVXX6kuU13gkT1=[hidden email]>
    Content-Type: text/plain; charset="utf-8"

    Hi,

    Sorry for spamming. Yes, I used 10-fold-cross validation.

    Santosh

    On Tue, May 2, 2017 at 12:00 PM, Santosh Bhosale <[hidden email]>
    wrote:

    > Hi Eibe,
    >
    > I tried the one you said below
    >
    > "Try the AttributeSelectedClassifier with WrapperSubsetEval, choosing
    > RandomForest as the evaluator in WrapperSubsetEval and also as the base
    > classifier in AttributeSelectedClassifier. The default BestFirstSearch
    > method will probably work fine on your data because it is quite a small
    > dataset"
    >
    > The SPSS, I just used for ROC curve plotting not for classification. So
    > the only concern is why such discrepancy in the AUCs. Did I make the
    > correct use of WEKA? Did my CSV input ot WEKA was correct?
    >
    > Thanks
    > Santosh
    >
    >
    >
    > On Tue, May 2, 2017 at 11:52 AM, Eibe Frank <[hidden email]> wrote:
    >
    >> Which classifier did you use in WEKA and SPSS and which evaluation
    >> method? 10-fold cross-validation?
    >>
    >> Cheers,
    >> Eibe
    >>
    >> On 2/05/2017, at 6:58 PM, Santosh Bhosale <[hidden email]>
    >> wrote:
    >>
    >> Hi Eibe,
    >>
    >> I tried your mentioned workflow. In result, WEKA showed a panel of 13
    >> attributes (Protein biomarkers) classifying cases from controls. On the
    >> same data, I drew ROC curve using WEKA, which gave AUC value of 0.937. But
    >> when I took the same combination and drew the ROC curve using SPSS, it was
    >> giving me AUC of 0.73. I am not understanding this discrepancy.
    >>
    >> Please see attached example of CSV file used as input for WEKA.
    >>
    >> Thanks in advance
    >> Santosh
    >>
    >> On Wed, Apr 26, 2017 at 12:18 PM, Eibe Frank <[hidden email]> wrote:
    >>
    >>> Try the AttributeSelectedClassifier with WrapperSubsetEval, choosing
    >>> RandomForest as the evaluator in WrapperSubsetEval and also as the base
    >>> classifier in AttributeSelectedClassifier. The default BestFirstSearch
    >>> method will probably work fine on your data because it is quite a small
    >>> dataset.
    >>>
    >>> Cheers,
    >>> Eibe
    >>>
    >>> > On 26 Apr 2017, at 00:39, Santosh Bhosale <[hidden email]>
    >>> wrote:
    >>> >
    >>> > Dear All,
    >>> >
    >>> > I did following steps in WEKA.
    >>> >
    >>> > - Uploaded the data in CSV file format
    >>> > - Ran classifier using J48 and RandomForest
    >>> > - J48 gave about 77% correctly classified instances
    >>> > - RandomForest gave about 84% correctly classified instances
    >>> >
    >>> > I had 264 instances and 24 attributes. However, I was not able to
    >>> pinpoint which combination of attributes had given the best classification
    >>> of cases from controls.
    >>> >
    >>> > Any help would be highly appreciated.
    >>> >
    >>> > Thanks
    >>> > Santosh
    >>> >
    >>> > On Tue, Apr 25, 2017 at 9:52 AM, Santosh Bhosale <
    >>> [hidden email]> wrote:
    >>> > Hi Eibe.
    >>> >
    >>> > Thanks
    >>> >
    >>> > On Tue, Apr 25, 2017 at 6:29 AM, Eibe Frank <[hidden email]>
    >>> wrote:
    >>> > You should probably learn about some basics of machine learning first.
    >>> There are some free on-line courses based on WEKA here:
    >>> >
    >>> >   https://weka.waikato.ac.nz/explorer
    >>> >
    >>> > Cheers,
    >>> > Eibe
    >>> >
    >>> > > On 25 Apr 2017, at 00:32, Santosh Bhosale <[hidden email]>
    >>> wrote:
    >>> > >
    >>> > > Hi All,
    >>> > >
    >>> > > I am proteomics expert and new to machine learning. I have protein
    >>> expression data between cases and controls where I have already found
    >>> significant markers. Now I want to predict a panel of markers which will
    >>> best classify cases from controls. I am not sure how to do that.
    >>> > >
    >>> > > It will be really good if someone urgently helps me in the context
    >>> of how the data-structure to be and what sort of pipeline to follow. So
    >>> using this information I can plot the ROC curve to best classify cases from
    >>> controls.
    >>> > >
    >>> > > Thanks in advance
    >>> > >
    >>> > > -Santosh
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    >> <exampleOfWEKAInput.csv>
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