Your SlideShare is downloading. ×
  • Like
  • Save
WEKA: The Experimenter
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply

WEKA: The Experimenter

  • 3,319 views
Published

Introduction to The Experimenter in Weka

Introduction to The Experimenter in Weka

Published in Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
3,319
On SlideShare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. The Experimenter
  • 2. Introduction
    The Experimenter enables you to set up large-scale experiments, start them running, leave them, and come back when they have finished and analyze the performance statistics that have been collected
    They automate the experimental process
    The statistics can be stored in ARFF format
    It allows users to distribute the computing load across multiple machines using Java RMI
  • 3. Introduction
  • 4. Demonstration
    We will compare the J48 decision tree method with the baseline methods OneR
    Steps:
    First click New to start a new experiment
    Then, on the line below, select the destination for the results
    Underneath, select the datasets
    To the right of the datasets, select the algorithms to be tested. Click Add new to get a standard Weka object editor from which you can choose and configure a classifier. Repeat this operation to add the two classifiers
  • 5. Demonstration
    To run the experiment click on the Run tab and then click on the Start button. The result will be stored on the output file
    To analyze the two selected classifiers , go to the Analyse tab and click on Experiment
    The symbol placed beside a result indicates that it is statistically better (v) or worse (*) than the baseline scheme
    At the bottom of column 2 are counts (x/y/z) of the number of times the scheme was better than (x), the same as (y), or worse than (z) the baseline scheme on the datasets used in the experiment
  • 6. Demonstration
  • 7. Visit more self help tutorials
    Pick a tutorial of your choice and browse through it at your own pace.
    The tutorials section is free, self-guiding and will not involve any additional support.
    Visit us at www.dataminingtools.net