pbvillaflores | 25 Apr 09:56 2014
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Threshold curve ARFF file questions


In the ARFF file result of visualizing threshold curve from running a Weka
classifier, why does the first record have a high value in True Positives
and False Positives value?

I used a 4.5K input file and the True Positives start at 500 and the False
Positives start at 500 and the False Positives start at 4000. Why do these
counters not start at zero on the first record of the ARFF file?

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Lisa Kolibar | 25 Apr 07:13 2014
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Bolt Dataset

I am taking my first class in data mining.  Our assignment is to analyze the Bolt assembly data.  I've converted the data set from numeric to nominal and I don't understand the output.  Can someone please help me by providing an explanation of how to interpret the data below.  Full model output is attached.

I've been looking for an answer for hours and appreciate any help you can provide.  Thanks, Lisa


SPEED1 = '(-inf-2.4]'
|   NUMBER2 = '(-inf-0.2]': '(-inf-15.522]' (8.0/4.0)
|   NUMBER2 = '(0.2-0.4]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(0.4-0.6]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(0.6-0.8]': '(15.522-23.724]' (0.0)
=== Run information ===

Scheme:weka.classifiers.trees.J48 -C 0.25 -M 2
Relation:     bolts-weka.filters.unsupervised.attribute.Discretize-B10-M-1.0-Rfirst-last-weka.filters.unsupervised.attribute.Discretize-unset-class-temporarily-B10-M-1.0-Rfirst-last-weka.filters.unsupervised.attribute.Remove-R7
Instances:    40
Attributes:   7
              RUN
              SPEED1
              TOTAL
              SPEED2
              NUMBER2
              SENS
              T20BOLT
Test mode:10-fold cross-validation

=== Classifier model (full training set) ===

J48 pruned tree
------------------

SPEED1 = '(-inf-2.4]'
|   NUMBER2 = '(-inf-0.2]': '(-inf-15.522]' (8.0/4.0)
|   NUMBER2 = '(0.2-0.4]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(0.4-0.6]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(0.6-0.8]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(0.8-1]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(1-1.2]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(1.2-1.4]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(1.4-1.6]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(1.6-1.8]': '(15.522-23.724]' (0.0)
|   NUMBER2 = '(1.8-inf)': '(15.522-23.724]' (8.0/2.0)
SPEED1 = '(2.4-2.8]': '(-inf-15.522]' (0.0)
SPEED1 = '(2.8-3.2]': '(-inf-15.522]' (0.0)
SPEED1 = '(3.2-3.6]': '(-inf-15.522]' (0.0)
SPEED1 = '(3.6-4]': '(-inf-15.522]' (8.0/1.0)
SPEED1 = '(4-4.4]': '(-inf-15.522]' (0.0)
SPEED1 = '(4.4-4.8]': '(-inf-15.522]' (0.0)
SPEED1 = '(4.8-5.2]': '(-inf-15.522]' (0.0)
SPEED1 = '(5.2-5.6]': '(-inf-15.522]' (0.0)
SPEED1 = '(5.6-inf)': '(64.734-72.936]' (16.0/9.0)

Number of Leaves  : 	19

Size of the tree : 	21

Time taken to build model: 0 seconds

=== Stratified cross-validation ===
=== Summary ===

Correctly Classified Instances          20               50      %
Incorrectly Classified Instances        20               50      %
Kappa statistic                          0.3344
Mean absolute error                      0.125 
Root mean squared error                  0.2703
Relative absolute error                 76.6386 %
Root relative squared error             95.3832 %
Total Number of Instances               40     

=== Detailed Accuracy By Class ===

               TP Rate   FP Rate   Precision   Recall  F-Measure   ROC Area  Class
                 0.714     0.231      0.625     0.714     0.667      0.769    '(-inf-15.522]'
                 0.444     0.097      0.571     0.444     0.5        0.636    '(15.522-23.724]'
                 0         0          0         0         0          0.224    '(23.724-31.926]'
                 0         0.054      0         0         0          0.653    '(31.926-40.128]'
                 0         0          0         0         0          ?        '(40.128-48.33]'
                 0         0          0         0         0          ?        '(48.33-56.532]'
                 0         0          0         0         0          0.346    '(56.532-64.734]'
                 0.857     0.273      0.4       0.857     0.545      0.732    '(64.734-72.936]'
                 0         0          0         0         0          0.684    '(72.936-81.138]'
                 0         0          0         0         0          0.658    '(81.138-inf)'
Weighted Avg.    0.5       0.154      0.417     0.5       0.441      0.676

=== Confusion Matrix ===

  a  b  c  d  e  f  g  h  i  j   <-- classified as
 10  2  0  1  0  0  0  1  0  0 |  a = '(-inf-15.522]'
  4  4  0  0  0  0  0  1  0  0 |  b = '(15.522-23.724]'
  0  0  0  1  0  0  0  1  0  0 |  c = '(23.724-31.926]'
  2  0  0  0  0  0  0  1  0  0 |  d = '(31.926-40.128]'
  0  0  0  0  0  0  0  0  0  0 |  e = '(40.128-48.33]'
  0  0  0  0  0  0  0  0  0  0 |  f = '(48.33-56.532]'
  0  0  0  0  0  0  0  1  0  0 |  g = '(56.532-64.734]'
  0  1  0  0  0  0  0  6  0  0 |  h = '(64.734-72.936]'
  0  0  0  0  0  0  0  2  0  0 |  i = '(72.936-81.138]'
  0  0  0  0  0  0  0  2  0  0 |  j = '(81.138-inf)'

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monisha kanakaraj | 25 Apr 04:34 2014
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Training using HMM and Testing using SVM

Hi, 

    Is it possible in weka to perform training on dataset with HMM and saving that model and testing datasets using SVM applying the saved HMM model? If yes how to d it?

     I have found this concept from an IEEE paper. 
     But I do not know how to do it. Is it same as how we load a pre-saved model (say HMM) and run re evaluated test test data on that (using same function say HMM) or something different ?
   
     Kindly help.   
    



Thanks & Regards,
Monisha Kanakaraj
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Charles Hamdani | 24 Apr 16:19 2014
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How to get all rules using apriori algorithm

Hi all,

I have 325 data in CSV format and I want to get all rules from this data with apriori algorithm using WEKA, can anyone help me ?? Thanks before...

Regards,
Charles

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Mark Hall | 24 Apr 11:01 2014
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New Weka 3.6.11 and 3.7.11 releases

Hi everyone!

New versions of Weka are available for download from the Weka homepage:

* Weka 3.6.11 - stable book 3rd edition version. It is available as ZIP,
with Win32 installer, Win32 installer incl. JRE 1.7.0_25, Win64 installer,
Win64 installer incl. 64 bit JRE 1.7.0_25 and Mac OS X application (both
Oracle and Apple JVM versions).

* Weka 3.7.11 - development version. It is available as ZIP, with Win32
installer, Win32 installer incl. JRE 1.7.0_25, Win64 installer, Win64
installer incl. 64 bit JRE 1.7.0_25 and Mac OS X application (both Oracle
and Apple JVM versions).

Both versions contain a significant number of bug fixes, it is recommended
to upgrade to the new versions. Stable Weka 3.6 receives bug fixes only.
The development version receives bug fixes and new features.

Weka homepage:
http://www.cs.waikato.ac.nz/~ml/weka/

Pentaho data mining community documentation:
http://wiki.pentaho.com/display/Pentaho+Data+Mining+Community+Documentation

Packages for Weka>=3.7.2 can be browsed online at:
http://weka.sourceforge.net/packageMetaData/

Note: It might take a while before Sourceforge.net has propagated all the
files to its mirrors.

Note 2: Changes to Weka 3.7.11 will necessitate new releases of a number
of packages. These will be made via the package manager over the next
week. Please refrain from reporting any problems with packages until after
the updates have been released.

What's new in 3.7.11?

Some highlights
---------------

In core weka:

* Bagging and RandomForest are now faster if the base learner is a
WeightedInstancesHandler
* Speed-ups for REPTree and other classes that use entropy calculations
* Many other code improvements and speed-ups
* Additional statistics available in the output of LinearRegression and
SimpleLinearRegression. Contributed by Chris Meyer
* Reduced memory consumption in BayesNet
* Improvements to the package manager: load status of individual packages
can now be toggled to prevent a package from loading; "Available" button
now displays the latest version of all available packages that are
compatible with the base version of Weka
* RandomizableFilteredClassifier
* Canopy clusterer
* ImageViewer KnowledgeFlow component
* PMML export support for Logistic. Infrastructure and changes contributed
by David Person
* Extensive tool-tips now displayed in the Explorer's scheme selector tree
lists
* Join KnowledgeFlow component for performing an inner join on two
incoming streams/data sets

In packages:

* IWSSembeded package, contributed by Pablo Bermejo
* CVAttributeEval package, contributed by Justin Liang
* distributedWeka package for Hadoop
* Improvements to multiLayerPerceptrons and addtion of MLPAutoencoder
* Code clean-up in many packages

As usual, for a complete list of changes refer to the changelogs.

Cheers,
The Weka Team

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lmsasu | 24 Apr 08:57 2014
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How is the forecast model created?

Hello all,

There is a Weka plugin for time series prediction. Is there any
documentation on how is the predictive model built, based on the regression
methods?

Thanks,
Lucian 

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Indrajit Mandal | 24 Apr 08:11 2014

Finding individual errors in each subclasses.

Hi every one,

Lets say a dataset has 4 subclasses- C1,C2, C3,C4.

I would like to find out the individual training and testing errors.

Normally we can see the overall learning errors of the overall classes.

Any help in this would be appreciated.

Best,
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Omer Barak | 23 Apr 17:54 2014

a "dummy" classifier that outputs a fixed class distribution

Hello,
I would like to create a learning scheme that classifies instances by
averaging probabilities from two sources:

• a learned model.
• a fixed probability value for each class (given by a domain expert).

The final classification will be the class with the highest (averaged)
probability.

Since I don't code in Java I figured the best way is to use the built
in weka.classifiers.meta.vote, and give it the target classifier plus
another "dummy" classifier that always outputs that same, fixed, class
distribution.
I want to know if there exist such builtin "dummy" classifier that allows me
to do this? (or any other suggestion to do this without Java).

Thanks,
Omer

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Indrajit Mandal | 23 Apr 19:19 2014

Question on Paired Corrected T Tester

Hi Eibe

First I wish to thank wholeheartedly to the WEKA team for their marvellous work for answering the queries of weka users in 
very less time. HATS OFF to your team.

I have a question to ask.

What is the difference between 'T-test' and 'corrected T test' as it is used in weka ?

What is the correction being done to T-test?

Any pointers or web links or any base research papers to this is highly appreciated.


Thanks in advance.


Best,



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cui | 23 Apr 12:04 2014
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weka Naive Bayes using weight

Hello

If I understand correctly, the default naive bayes in weka assumes all features have the same weights.

Do you have any idea by tuning some parameters or other modules, if we can run the naive bayes on features assigning different weights?


Thanks very much
cui


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Dicheva, Darina | 23 Apr 01:03 2014

TF-IDF in the StringToWordVector Filter

Hello all,

How can I specify the standard TF-IDF measure in the StringToWordVector Filter? Apart from IDFTransform
should I set to True anything else (e.g.  TFTransform, normalizeDocLength)? 

Thanks much,
Darina

---
Darina Dicheva
Winston-Salem State University
601 S. Martin Luther King Jr. Drive
Winston Salem, NC 27110

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Gmane