View mldata api demo (public)

2010-11-30 14:19 by cong | Version 1 | Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty StarEmpty Star
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Summary

Example of how to use data and task

License
CC-BY-SA 3.0
Tags
api demo
Feature Processing
No preprocessing or scaling of features are done
Parameters
SVM C=1.0 with Gaussian kernel width=1.0
Operating System
agnostic
Code

See source code, clientapi/api_demo.sh clientapi/mlprocess.py

Software Packages

mldata-utils available at: http://mloss.org/software/view/262/

Completeness of this item currently: 100%.
Description

Demonstrate how to use the application programming interface (API) of mldata.org.

The code detects the task type {binary classification, multiclass, regression} and creates the appropriate SVM.

Location of labels vs. examples, as well as the training and test splits are all taken automatically from the task file.

URLs
http://mloss.org/software/view/262/
Publications
    revision 1
    by cong on 2010-11-30 14:19

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    Method mldata api demo has been applied the following tasks:

    SubmitterTask (version)CurveScoreDate
    cong pima binclass (1) - 0.817164179104  predictions 2010-11-30 14:20
    cong pima binclass roc (3) 0.791336570406  predictions 2010-11-30 14:22
    cong iris multiclass (1) - 1.0  predictions 2010-11-30 14:22
    cong pima binclass (1) - 0.817164179104  predictions 2013-06-27 11:19
    cong boston housing scaled regression (1) - 0.396992035548  predictions 2010-12-02 18:00
    cong boston housing regression (1) - 8.56191478601  predictions 2010-12-02 18:01
    To submit a result, please sign in.

    Disclaimer

    We are acting in good faith to make datasets submitted for the use of the scientific community available to everybody, but if you are a copyright holder and would like us to remove a dataset please inform us and we will do it as soon as possible.

    Data | Task | Method | Challenge

    Acknowledgements

    This project is supported by PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning)
    PASCAL Logo
    http://www.pascal-network.org/.