International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
www.ijsr.net | Most Trusted Research Journal Since Year 2012

ISSN: 2319-7064



Research Paper | Computer Science & Engineering | India | Volume 4 Issue 6, June 2015

Analysis on PLSR in Contrast with PCR

Abhimanyu Kumar

In this paper, we have described a very efficient tool for data mining, i.e., predictive data mining techniques. Partial Least Square Regression, predictive technique is briefly discussed and is compared with Principal Component Regression, another predictive technique. We have implemented Partial Least Square Regression (PLSR) technique on the Gasoline dataset. From other researchers, we have taken the result of Principal Component Regression (PCR) technique and compared our result of PLSR with the PCRs result, and it is observed that the result of PLSR is much more significant than the PCR, in respect to R-square error (R-sq) criteria for model comparison. Also we have seen that the 3 components of PLSR gives better result as compared to 4 components of PCR.

Keywords: KDD- knowledge Discovery database, PDM Predictive Data Mining, PCR Principal Component Regression, PLSR Partial Least Square Regression, PCA Principal Component Analysis

Edition: Volume 4 Issue 6, June 2015

Pages: 2369 - 2371

Share this Article

How to Cite this Article?

Abhimanyu Kumar, "Analysis on PLSR in Contrast with PCR", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=SUB155741, Volume 4 Issue 6, June 2015, 2369 - 2371

31 PDF Views | 29 PDF Downloads

Download Article PDF

Similar Articles with Keyword 'PCA Principal Component Analysis'

Research Paper, Computer Science & Engineering, India, Volume 4 Issue 6, June 2015

Pages: 2369 - 2371

Analysis on PLSR in Contrast with PCR

Abhimanyu Kumar

Share this Article

Research Paper, Computer Science & Engineering, India, Volume 4 Issue 6, June 2015

Pages: 2579 - 2582

Content Based Image Retrieval and Classification Using Principal Component Analysis

Roshani Mandavi, Kapil Kumar Nagwanshi

Share this Article



Top