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10.1186/s12859-016-1228-x http://scihub22266oqcxt.onion/10.1186/s12859-016-1228-x C5009551!5009551 !27586051     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27586051 &cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\27586051 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Bioinformatics 2016 ; 17 (1 ): 331 Nephropedia Template TP {{tp|p=27586051 |t=2016. The parameter sensitivity of random forests |pdf=|usr=}}{{27586051 }} gab.com Text The parameter sensitivity of random forests JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27586051 #FOAvip BACKGROUND: The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to numerous assertions regarding the performance reliability of the default parameters, many RF models are fit using these values. However there has not yet been a thorough examination of the parameter-sensitivity of RFs in computational genomic studies. We address this gap here. RESULTS: We examined the effects of parameter selection on classification performance using the RF machine learning algorithm on two biological datasets with distinct p/n ratios: sequencing summary statistics (low p/n) and microarray-derived data (high p/n). Here, p, refers to the number of variables and, n, the number of samples. Our findings demonstrate that parameterization is highly correlated with prediction accuracy and variable importance measures (VIMs). Further, we demonstrate that different parameters are critical in tuning different datasets, and that parameter-optimization significantly enhances upon the default parameters. CONCLUSIONS: Parameter performance demonstrated wide variability on both low and high p/n data. Therefore, there is significant benefit to be gained by model tuning RFs away from their default parameter settings. Twit Text FOAVip The parameter sensitivity of random forests ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=27586051 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27586051 #FOAvip English Wikipedia <ref>{{Cite pmid|27586051 }}</ref> The parameter sensitivity of random forests #MMPMID27586051 Huang BF ; Boutros PC BMC Bioinformatics 2016[Sep]; 17 (1 ): 331 PMID27586051 show ga BACKGROUND: The Random Forest (RF) algorithm for supervised machine learning is an ensemble learning method widely used in science and many other fields. Its popularity has been increasing, but relatively few studies address the parameter selection process: a critical step in model fitting. Due to numerous assertions regarding the performance reliability of the default parameters, many RF models are fit using these values. However there has not yet been a thorough examination of the parameter-sensitivity of RFs in computational genomic studies. We address this gap here. RESULTS: We examined the effects of parameter selection on classification performance using the RF machine learning algorithm on two biological datasets with distinct p/n ratios: sequencing summary statistics (low p/n) and microarray-derived data (high p/n). Here, p, refers to the number of variables and, n, the number of samples. Our findings demonstrate that parameterization is highly correlated with prediction accuracy and variable importance measures (VIMs). Further, we demonstrate that different parameters are critical in tuning different datasets, and that parameter-optimization significantly enhances upon the default parameters. CONCLUSIONS: Parameter performance demonstrated wide variability on both low and high p/n data. Therefore, there is significant benefit to be gained by model tuning RFs away from their default parameter settings. |*Algorithms [MESH] |Computational Biology/*methods [MESH] |Models, Theoretical [MESH]The parameter sensitivity of random forests (epmc).pdf DeepDyve Pubget Overpricing