• Content
• Home
• Tools
• SiRMS
• SiRMS-SPCI
• PMapper
• CReM
• Random Forest
• Tutorials
• R tutorials
• Python tutorials
• RDKit tutorials
• QSAR tutorials
• Opended positions
• PhD positions
• Publications
• Presentations
• Publications
• Contacts

Random Forest

Random Forest is a relatively young but powerful statistical method. It proved its applicability to QSAR tasks solutions. Random Forest have a lot of advantages which allow to effectively analyzing huge datasets:

• fast learning algorithm,
• internal procedure of estimation of predictive ability,
• robust to over-fitting and “noise” in data.

More detailed description of Random Forest method and examples of QSAR tasks solved by this method can be found in a short presentation here.

We developed Random Forest software (CF) which suits for QSAR application. Main features of the CF program are:

• single and multi-task learning algorithms,
• applicability domain,
• Y scrambling procedure,
• virtual screening,
• multithreading.

The lastest version of CF program (fully functional, ver. 2.13, updated 25-12-2012) can be downloaded here
OS: Windows XP/Vista/7

The lastest version of the manual for CF program can be downloaded here

Original article of Random Forest method:
Breiman L., Random Forests. Machine Learning 2001, 45, (1), 5-32. - link

Our paper "Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity"
J. Chem. Inf. Model., 2009, 49 (11), pp 2481–2488 - link