Ashish Dutt | 2 Sep 17:51 2014
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Is there an easy method to filter punctuation marks from a dataset?

Dear all,
I have a dataset with a million rows in it and about 13 columns.
I have a problem loading it in Weka and I know the reason is caused by punctuation marks in a particular column.
I'm stuck at the first step where the data file needs to convereted into ARFF format. Weka will not load the datafile throwing a java.io exception error.
Is there any method to fix this or do I have to manually clean the datafile in excel?
Any pointers or help in this direction would be appreciated.

Cheers
Ashish

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Samir Sarsam | 1 Sep 06:55 2014
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my question:In the other words, is it compulsory having same number of instances for all attributes in order to deal with weka), and what should we do if the number of data are different from attribute to other?

Hi,

Let's say my data are consisted of three columns (like the table in the bottom of this page which is already attached also) where each of them has different number of data. My question: can weka deal with these data normally in terms of applying classifiers, for example, can we apply the linear regression algorithm directly over this data or we have to do  any adjustment  first? In the other words, is it compulsory having same number of instances for all  attributes in order to deal with weka's (e.g. weka's classifiers), and what should we do if the number of data are different from attribute to other?
Thanks.

Best Regards,
Sam 

X

Y

Z (class)

12

12

454

33

33

123

54

54

6431

55

55

12

39

39

33

100

100

54

 

12

55

 

77

39

 

55

100

 

113

12

 

46

77

 

34

55

 

567

113

 

354

46

 

123

34

 

 

567

 

 

354

 

 

1000

 

 

453

 

 

758

 

 

235

 

 

788

 
Attachment (Table.docx): application/vnd.openxmlformats-officedocument.wordprocessingml.document, 14 KiB
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Patrick Sanches | 31 Aug 19:06 2014
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Unsubscribing

help

--
Thanks,

Patrick G. Sanches
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Samir Sarsam | 31 Aug 19:22 2014
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How to save the weka results' figures to insert them later in Microsoft Word?

Hi,

My question is: in order to do report documentation, what is the best way to save only the figure (as jpeg for instance) that we have already visualised from the results?

Thanks.

Best Regards,
Sam

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Sairam Sai | 31 Aug 16:59 2014
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To know how to overcome the Network adapter error.

Thanks to Mr./Ms Guillaume MULLER for helping me in the search. I've followed the procedure in the following link to connect WEKA to the DB (http://forums.pentaho.com/showthread.php?66622-Anyone-Successfully-using-weka-to-pull-data-from-Oracle ). But I still am getting
connection problem with WEKA :
exception: java.sql.SQLException: Io exception: The Network Adapter could not establish the connection


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Maha | 31 Aug 15:47 2014
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need help in BF tree interpretation

Hi weka people

please can you help me to interpret this result after using BFtree

 clin.gr1=(A)|(T)|(B)|(C)|(N)|(I)|(D)|(O)|(.): I(20.28/0.51)
|  |  |  |  clin.gr1!=(A)|(T)|(B)|(C)|(N)|(I)|(D)|(O)|(.): F(0.47/0.03)

what Exclamation mark (clin.gr1!) mean ,( |  )   and   I(0.47/0.03) mean?

thanks alot

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Samir Sarsam | 30 Aug 21:45 2014
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When can we use Naive Bayes

Hi,

My question is about doing prediction with Naive Bayes.

In the "Iris" example of weka package, there are 5 attributes where all are from the type "Numeric" except the last one (class) which is for the type (Nominal). In order to predict the attribute "class", if the aim is using "Linear regression" to so, this can not be happened since the attribute "class" is from the type "Nominal" and Linear regression does not handle this type. Thus, my question: is there any problem in using "Naive Bayes" instead of "Linear regression" since it can deal with the Nominal type?

Thanks.

Best Regards,
Sam

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Prathibha | 30 Aug 08:16 2014
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Output predictions with IBk, RWeka

Hello, 

I am trying to output predictions for IBk classifier on RWeka. 

On Weka Explorer: 
Classifier -> More Options -> Output Predictions 

On RWeka: 
predict_Weka_classifier : predict(object, newdata = NULL, type = c("class",
"probability"), ...) 

Is this the right function to use on RWeka to output predicted classes ? 
According to my results, this is not the right way. Because the predicted
classes are same as original classes and so, do not match with the results
from evaluate_Weka_Classifier.

Please help by pointing to the right way to output IBk classifier
predictions on RWeka. 

Thanks much. 

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Prathibha Datta Kumar | 30 Aug 08:08 2014
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Output predictions with IBk, RWeka

Hello, 

I am trying to output predictions for IBk classifier on RWeka. 

On Weka Explorer: 
Classifier -> More Options -> Output Predictions 

On RWeka: 
predict_Weka_classifier : predict(object, newdata = NULL, type = c("class", "probability"), ...) 

Is this the right function to use on RWeka to output predicted classes ? 
According to my results, this is not the right way. Because the predicted classes are same as original classes and so, do not match with the results from evaluate_Weka_Classifier.

Please help by pointing to the right way to output IBk classifier predictions on RWeka. 

Thanks much. 
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Samir Sarsam | 30 Aug 12:27 2014
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My question is about doing prediction with Naive Bayes

Dear Sir/Madam,

My question is about doing prediction with Naive Bayes.

In the "Iris" example of weka package, there are 5 attributes where all are from the type "Numeric" except the last one (class) which is for the type (Nominal). In order to predict the attribute "class", if the aim is using "Linear regression" to so, this can not be happened since the attribute "class" is from the type "Nominal" and Linear regression does not handle this type. Thus, my question: is there any problem in using "Naive Bayes" instead of "Linear regression" since it can deal with the Nominal type?

Thanks.

Best Regards,
Sam
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Sairam Sai | 29 Aug 12:19 2014
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Re: Wekalist Digest, Vol 138, Issue 54

I am using WEKA 3.7 and Oracle 10g as my DBMS. I've followed the procedure in the following link to connect WEKA to the DB. But I am getting connection problem with WEKA : exception: java.sql.SQLException: No suitable driver found for jdbc:oracle:thin: <at> localhost:1521:XE .

http://www.databaseskill.com/3033038/


On Wed, Aug 27, 2014 at 2:28 AM, <wekalist-request <at> list.waikato.ac.nz> wrote:
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Today's Topics:

   1. Re: Instances on tree diagram in HierarchicalClusterer
      results (bug?) (Jennifer Diaz)
   2. Fwd: To know about DB Connectivity to WEKA (Sairam Sai)
   3. Re: Unable to get the values of Support Vectors in SVM
      Regression (Eibe Frank)
   4. Re: To know about DB Connectivity to WEKA (Eibe Frank)
   5. Re: Build model from complete data or from split data     (80%)?
      (Eibe Frank)


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

Message: 1
Date: Tue, 26 Aug 2014 10:38:56 -0400
From: Jennifer Diaz <andbeonetraveler <at> gmail.com>
To: "Weka machine learning workbench list."
        <wekalist <at> list.waikato.ac.nz>
Subject: Re: [Wekalist] Instances on tree diagram in
        HierarchicalClusterer   results (bug?)
Message-ID: <3D6DD2E0-9707-4659-A8D3-CEAF64E1D916 <at> gmail.com>
Content-Type: text/plain; charset=us-ascii

Awesome, thanks.

Really not sure why those didn't come up in my search. I did about as thorough a search as I could think of in google, the wiki, the archives, and I looked at ALL my search results in the archives...

Those posts seem to present solutions for the API, so for posterity, here's how to do it in the Explorer GUI after loading a .csv file:
-Preprocess>Filter>Choose>unsupervised>attribute>NominalToString
-Click on 'NominalToString' when it appears in the filter box, and set the index to the attribute containing IDs or classes for labeling the leaves
-If you convert more than one attribute to a string, the one to be used for labeling must be first
-Under Cluster, do NOT ignore the string attribute
Labels in the string attribute appear in the newick string and on the dendrogram.

On Aug 26, 2014, at 4:12 AM, Eibe Frank wrote:

> Check these posts:
>
> http://weka.8497.n7.nabble.com/Question-about-the-hierarchicalclusterer-result-td16947.html
> http://weka.8497.n7.nabble.com/hierarchical-clustering-newick-format-output-td30567.html
>
> Cheers,
> Eibe
>
> On 26 Aug 2014, at 17:01, andbeonetraveler <andbeonetraveler <at> gmail.com> wrote:
>
>> I believe this has been asked once before, but never answered:
>> http://weka.8497.n7.nabble.com/Question-on-Hierarchical-Clustering-td7186.html
>>
>> I am using the HierarchicalClusterer algorithm /from the Explorer GUI/ with
>> weka 3-7-11 for Mac. I would like to get the location of each instance on
>> the resulting dendrogram (i.e.
>> http://www.biomedcentral.com/content/figures/1471-2105-5-126-1-l.jpg) so
>> that I can align it to a heatmap (i.e.
>> http://www.nature.com/srep/2011/110629/srep00025/images/srep00025-f5.jpg).
>>
>> That's a pretty common thing to do, isn't it?
>>
>> As an example, I ran HierarchicalClusterer on the sample iris.arff data:
>>
>> Scheme:weka.clusterers.HierarchicalClusterer -N 1 -L SINGLE -P -A
>> "weka.core.EuclideanDistance -R first-last"
>> Relation:     iris
>>
>> The resulting tree looks like this:
>> Cluster 0
>> ((((((((((((((((((((((0.2:0.03254,0.2:0.03254):0.00913,(0.3:0.03254,0.3:0.03254):0.00913):0.00332,((0.2:0.02778,0.2:0.02778):0.00476,0.2:0.03254):0.01244):0,0.2:0.04498):0.0051,0.2:0.05008):0.00364,0.2:0.05371):0.00437,(0.2:0.05085,0.2:0.05085):0.00724):0.01535,(0.5:0.06731,0.4:0.06731):0.00612):0.00188,0.2:0.07531):0.00196,0.3:0.07728):0.00536,((((((0.2:0.04383,0.2:0.04383):0.00625,0.3:0.05008):0,0.1:0.05008):0.00279,(((((0.2:0.03254,0.2:0.03254):0.01129,0.2:0.04383):0.00116,0.2:0.04498):0.0051,0.2:0.05008):0.00279,((0.1:0,0.1:0):0,0.1:0):0.05287):0):0.00522,0.2:0.05808):0.01919,((0.2:0.04498,0.2:0.04498):0.01549,0.1:0.06047):0.0168):0.00536):0.00165,0.2:0.08429):0.00356,(((0.2:0.02778,0.2:0.02778):0.04371,((0.3:0.04498,0.2:0.04498):0.01394,0.4:0.05893):0.01256):0.00809,0.4:0.07958):0.00826):0.00212,0.4:0.08996):0.00321,0.6:0.09317):0.00598,(0.4:0.0678,0.4:0.0678):0.03135):0.00292,0.3:0.10206):0.01316,0.2:0.11523):0.01375,(0.2:0.12263,(0.1:0.10346,0.2:0.10346):0.01917):0.
 0
> 06
>> 34):0.00241,0.4:0.13139):0.12414,0.3:0.25553):0.20714,(((((((((((((((((((((((((((((1.4:0.07344,(((1.5:0.06508,1.5:0.06508):0.00066,(1.4:0.05008,1.4:0.05008):0.01566):0.00224,1.3:0.06798):0.00546):0.00188,(1.3:0.07137,(1.3:0.05556,1.3:0.05556):0.01581):0.00395):0.00733,(1.5:0.07137,((1.4:0.04498,1.4:0.04498):0.01549,1.5:0.06047):0.01089):0.01127):0.00515,1.4:0.08779):0.00538,1.2:0.09317):0.00405,1.5:0.09722):0.0004,(1.5:0.05556,1.5:0.05556):0.04207):0.00152,(1.5:0.07344,1.6:0.07344):0.02571):0,1.6:0.09914):0.00219,1.5:0.10133):0.00073,1.6:0.10206):0.0014,(((((1.3:0.08333,1.3:0.08333):0.00613,((((1.3:0.06574,((1.3:0.05287,1.2:0.05287):0,(1.3:0.05287,(1.3:0.04498,1.3:0.04498):0.00789):0):0.01287):0.0077,(1.2:0.04498,1.2:0.04498):0.02845):0,1.2:0.07344):0.0093,(1.1:0.05287,(1.1:0.04498,1.0:0.04498):0.00789):0.02987):0.00672):0.0005,1.0:0.08996):0.00406,1.0:0.09402):0.00041,1.3:0.09443):0.00902):0.00268,1.7:0.10614):0.00342,((((((1.8:0.08784,((1.8:0.03254,1.8:0.03254):0.0254,1.
 8
> :
>> 0.05794):0.0299):0.00162,(1.9:0.08429,(1.8:0.05287,1.8:0.05287):0.03142):0.00518):0.00524,1.9:0.0947):0.01144,(2.2:0.09415,(2.1:0.04167,2.2:0.04167):0.05249):0.01199):0,(((1.8:0.07148,(1.8:0.05008,1.8:0.05008):0.02141):0.02614,(2.0:0.08504,2.0:0.08504):0.01258):0.00852,(((2.1:0.05287,2.1:0.05287):0.04475,((((2.3:0.04383,2.3:0.04383):0.03881,2.4:0.08264):0.00719,(2.3:0.07148,2.3:0.07148):0.01834):0.00487,2.5:0.0947):0.00292):0.00534,2.1:0.10296):0.00318):0):0.00129,2.1:0.10743):0.00214):0.00446,((2.5:0.08983,(2.4:0.06047,2.3:0.06047):0.02935):0.01175,2.3:0.10158):0.01245):0.01212,1.4:0.12614):0.00283,1.4:0.12897):0.00054,1.5:0.12951):0.00514,(((1.9:0,1.9:0):0.08779,2.0:0.08779):0.01089,2.0:0.09869):0.03597):0.01023,((1.5:0.09869,1.3:0.09869):0.00264,1.5:0.10133):0.04356):0.00338,(((2.1:0.09869,2.0:0.09869):0.02337,2.3:0.12206):0.01586,((1.8:0.07344,1.9:0.07344):0.05554,(1.8:0.12263,1.6:0.12263):0.00634):0.00895):0.01034):0.00275,1.8:0.15102):0.00299,2.3:0.15401):0.00606,(((
 1
> .
>> 0:0.05008,1.0:0.05008):0.04555,1.1:0.09562):0.03389,1.0:0.12951):0.03056):0.00969,1.0:0.16976):0.00916,2.4:0.17892):0.01985,2.5:0.19878):0.00086,1.7:0.19964):0.02884,(2.2:0.11232,2.0:0.11232):0.11615):0.23419)
>>
>>
>> Each leaf here is represented by a number such as 1.0, 2.5, 1.7, etc. I
>> would have expected it to be represented by instance number. Turns out in
>> this case, those numbers are petal width. The numbers are also the only
>> label displayed on the dendrogram under 'Visualize Tree'.
>>
>> From trying this on my own files, I figured out that the algorithm always
>> defaults to labeling the leaves by the second-to-last column in the file,
>> and it forces labeling numerically. (i.e. if I put nominal labels--for
>> example a class--in that location in the file, the algorithm converts the
>> names into integers and uses those to label the leaves).
>>
>> This seems like a bug to me, or at least an oversight. I can get around it
>> by organizing my file such that the second-to-last column contains integers
>> labeling the instances in a way that's meaningful to me. But it would be
>> much more useful to have the tree labeled by instance number or a set of
>> user-defined classes or IDs. And it would be nice to be able to use
>> non-numerical labels.
>>
>> I'd also like to be able to export those labels to a list in a file, in the
>> order that they appear on the dendrogram, so that I can align the dendrogram
>> to the rest of the figure.
>>
>> Is this fixable?
>>
>> Thanks!
>>
>>
>>
>>
>> --
>> View this message in context: http://weka.8497.n7.nabble.com/Instances-on-tree-diagram-in-HierarchicalClusterer-results-bug-tp32016.html
>> Sent from the WEKA mailing list archive at Nabble.com.
>> _______________________________________________
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------------------------------

Message: 2
Date: Tue, 26 Aug 2014 20:17:41 +0530
From: Sairam Sai <k.sairam94 <at> gmail.com>
To: wekalist <at> list.waikato.ac.nz
Subject: [Wekalist] Fwd: To know about DB Connectivity to WEKA
Message-ID:
        <CAOjgozYrgSV0+0yhZWJF=U_GczuYS6VRkzDEusLftgR2zBKunw <at> mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

---------- Forwarded message ----------
From: Sairam Sai <k.sairam94 <at> gmail.com>
Date: Tue, Aug 26, 2014 at 7:40 PM
Subject: To know about DB Connectivity to WEKA
To: contact <at> waikato.ac.nz


Greetings,

I am K Sairam, studying IV B.Tech IT, Tirupati. I am using ORACLE 10g as my
database to connect to WEKA Tool. And I am facing a connection problem. I
would be so grateful to you on this help.

Regards,
K Sairam
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Message: 3
Date: Wed, 27 Aug 2014 08:47:52 +1200
From: Eibe Frank <eibe <at> waikato.ac.nz>
To: "Weka machine learning workbench list."
        <wekalist <at> list.waikato.ac.nz>
Subject: Re: [Wekalist] Unable to get the values of Support Vectors in
        SVM     Regression
Message-ID: <4D8446C6-30BA-479E-968A-4DF7F4421832 <at> waikato.ac.nz>
Content-Type: text/plain; charset=windows-1252

The support vectors are the selected training instances, e.g. k[0] refers to the dot product of the instance to be classified and the first instance in the training data (after the preprocessing steps I mentioned). k[3] refers to the dot product with the fourth training instance, and so on.

Cheers,
Eibe

On 26 Aug 2014, at 22:28, Nikolaos Lampovas <nikos.lampovas <at> gmail.com> wrote:

> Dear Mr.Frank,
>
> I need the values of the support vectors, as for the coefficients i can retrieve them properly.
> In your book "Data mining machine learning tools and techniques" 3rd Edition p.465, there is an example with a polynomial kernel providing the values of the support vectors.
> I would like to be able to retrieve these values for both polynomial and rbf kernels.
> I tried the above example with weka 3.6.0 SVMreg, SMOreg and weka 3.7.11 with debugging option enabled and i got the results you mentioned.
> I couldn't get any results like those in the book.
> What should i do?
>
>
> Lampovas Nikolaos
> NTUA University
>
>
> On Tue, Aug 26, 2014 at 1:57 AM, Eibe Frank <eibe <at> waikato.ac.nz> wrote:
> SMORegImproved outputs the model in this form (if it?s non-linear):
>
> -0.23070646313957802 * k[0]
> -0.021636125123660564 * k[3]
> -0.03704530247229904 * k[5]
>
> Assuming your data doesn?t contain any missing class values or instances with weight zero (which are deleted before the model is built), the numbers in square brackets are the indices of the training instances used as support vectors.
>
> Note that the data is filtered through (a) .unsupervised.attribute.NominalToBinary and (b) ReplaceMissingValues before the model is built. If normalisation/standardization is turned on, then (c) Normalize/Standardize is also applied.
>
> Cheers,
> Eibe
>
> PS: It might be best to simply implement PMML output in SMORegImproved, as it?s done for Logistic:
>
>   http://weka.sourceforge.net/doc.dev/
>
> This would be a nice contribution. :-)
>
>
> On 22 Aug 2014, at 22:40, Nikolaos Lampovas <nikos.lampovas <at> gmail.com> wrote:
>
> > Dear Sir/Madam,
> >
> > In our application (opentox.ntua.gr), we are using the SVMreg classifier from weka v3.6.0 library for our SVMRegression algorithm.
> > For the models of this algorithm we would like to be able to produce their pmml files.
> > Using the .toString() function of the SVMreg classifier we get the coefficients properly but we also need to get the values of the support vectors.
> > In PMML (http://www.dmg.org/v4-1/SupportVectorMachine.html) they are defined by the <VectorInstance> tag.
> > We have tried the libSVM classifier and also the SMOreg classifier from the new weka library v3.7.11 but as far as we have seen the support vectors are not available.
> > How can we solve this issue?
> >
> >
> > Thank you for any help you can provide
> > Lampovas Nikolaos
> > NTUA University
> > _______________________________________________
> > Wekalist mailing list
> > Send posts to: Wekalist <at> list.waikato.ac.nz
> > List info and subscription status: http://list.waikato.ac.nz/mailman/listinfo/wekalist
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>
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------------------------------

Message: 4
Date: Wed, 27 Aug 2014 08:49:19 +1200
From: Eibe Frank <eibe <at> waikato.ac.nz>
To: "Weka machine learning workbench list."
        <wekalist <at> list.waikato.ac.nz>
Subject: Re: [Wekalist] To know about DB Connectivity to WEKA
Message-ID: <12B90217-6F7E-4B11-9473-CF0ADE2A127C <at> waikato.ac.nz>
Content-Type: text/plain; charset=us-ascii

What is the nature of your connection problem, i.e. what kind of exception do you get in the log? We need details to be able to help you.

Cheers,
Eibe

On 27 Aug 2014, at 02:47, Sairam Sai <k.sairam94 <at> gmail.com> wrote:

>
>
> ---------- Forwarded message ----------
> From: Sairam Sai <k.sairam94 <at> gmail.com>
> Date: Tue, Aug 26, 2014 at 7:40 PM
> Subject: To know about DB Connectivity to WEKA
> To: contact <at> waikato.ac.nz
>
>
> Greetings,
>
> I am K Sairam, studying IV B.Tech IT, Tirupati. I am using ORACLE 10g as my database to connect to WEKA Tool. And I am facing a connection problem. I would be so grateful to you on this help.
>
> Regards,
> K Sairam
>
> _______________________________________________
> Wekalist mailing list
> Send posts to: Wekalist <at> list.waikato.ac.nz
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> List etiquette: http://www.cs.waikato.ac.nz/~ml/weka/mailinglist_etiquette.html



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

Message: 5
Date: Wed, 27 Aug 2014 08:58:13 +1200
From: Eibe Frank <eibe <at> waikato.ac.nz>
To: "Weka machine learning workbench list."
        <wekalist <at> list.waikato.ac.nz>
Subject: Re: [Wekalist] Build model from complete data or from split
        data    (80%)?
Message-ID: <61E673E3-FB7C-4A6C-B44C-FC415E659F99 <at> waikato.ac.nz>
Content-Type: text/plain; charset=windows-1252


On 26 Aug 2014, at 21:59, Aljoscha Kindermann <a-kindermann <at> gmx.net> wrote:

> First of all thank you for your answer! Your remark concerning the
> oob-Error  is helpful, especially since the RandomForest was well
> performing in my preliminary experiments.
> I read some literature about it now, but I have a question: Is there a
> general statement that can be said about which values of oob are "good"
> and which are "bad??

The OOB estimate is just another error estimate, an alternative to evaluation on a test set. It generally shouldn?t be that different.

What?s good or bad depends on your application/dataset.

> A second question concerns the RandomForest itself: Is there any
> disadvantage in increasing the number of features setting? Is there
> anything that can be generally said? (I refer to e.g. Multilayer
> Perceptron where increasing the hidden nodes may lead to overfitting)

Yes, I?d say that by increasing the number of features that are randomly selected you increase the chance of overfitting.

Cheers,
Eibe

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

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End of Wekalist Digest, Vol 138, Issue 54
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Gmane