To Download: Click Here

Bias-Variance Estimator is a software tool that can be used to understand the contribution of two components of the prediction errors, namely, Bias (or systematic) error and Variance (or random) error, which are obtained from the developed model. This tool employs ‘bootstrapping as a re-sampling technique.

To Download and Run the Program

Click on the download link above (it will direct you to google drive) and then press "ctrl + S (Windows) or cmd+S (Macs)" to save as zip file. Extract the .zip file and click on .jar file to run the program.

Note: The program folder will consist of three folders "Data", "Lib" and "Output". For user convenience, user may keep input files in "Data" folder and may save output file in "Output" folder."Lib" folder consist of library files required for running the program. Check the format of training and test sets input files (.xlsx/.xls/.csv) before using the program (sample files are provided in Data Folder). *Manual is provided in the program folder.

File Format: Compound number (first column), Descriptors (Subsequent Columns), Activity/Property (Last column)

Reference for Bias-Variance Estimator

1.  http://alpinedata.com/wp-content/uploads/2015/12/ML-Whitepaper_12.29.pdf

2.  http://pareonline.net/getvn.asp?n=2&v=8

3.  http://pareonline.net/getvn.asp?v=18&n=11

4. http://scott.fortmann-roe.com/docs/BiasVariance.html (Bias and Variance are nicely explained)

5. https://theclevermachine.wordpress.com/2013/04/21/model-selection-underfitting-overfitting-and-the-bias-variance-tradeoff/

6. Gigerenzer, G.; Brighton, H., Homo heuristicus: Why biased minds make better  inferences. Top. Cogn. Sci. 2009, 1, (1), 107-143.

7. Belsley, D. A., Conditioning diagnostics: collinearity and weak data in regression. Wiley Online Library: New York, 1991.