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Title | RTextTools: a machine learning library for text classification - |
Description | Menu RTextTools: a machine learning library for text classification Blog About the Project Install How to Cite Documentation Classifying Breast Cancer as |
Keywords | r, rtexttools, machine learning, supervised learning, social science, political science, automatic classification, text classification |
WebSite | rtexttools.com |
Host IP | 199.34.228.100 |
Location | United States |
Site | Rank |
US$1,771,010
Last updated: 2023-05-14 11:05:22
rtexttools.com has Semrush global rank of 5,976,424. rtexttools.com has an estimated worth of US$ 1,771,010, based on its estimated Ads revenue. rtexttools.com receives approximately 204,348 unique visitors each day. Its web server is located in United States, with IP address 199.34.228.100. According to SiteAdvisor, rtexttools.com is safe to visit. |
Purchase/Sale Value | US$1,771,010 |
Daily Ads Revenue | US$1,635 |
Monthly Ads Revenue | US$49,044 |
Yearly Ads Revenue | US$588,521 |
Daily Unique Visitors | 13,624 |
Note: All traffic and earnings values are estimates. |
Host | Type | TTL | Data |
rtexttools.com. | A | 3600 | IP: 199.34.228.100 |
rtexttools.com. | NS | 3600 | NS Record: ns77.domaincontrol.com. |
rtexttools.com. | NS | 3600 | NS Record: ns78.domaincontrol.com. |
rtexttools.com. | MX | 3600 | MX Record: 10 mailstore1.secureserver.net. |
rtexttools.com. | MX | 3600 | MX Record: 0 smtp.secureserver.net. |
Menu RTextTools: a machine learning library for text classification Blog About the Project Install How to Cite Documentation Classifying Breast Cancer as Benign or Malignant Using RTextTools 2/11/2012 30 Comments RTextTools has largely been used for topic classification in the social sciences. However, recent discussions with researchers at various universities have demonstrated that the package can be applied to a host of problems in the natural sciences as well. One such application is using text classification to identify breast cancer masses as benign or malignant. Using the Wisconsin Diagnostic Breast Cancer Dataset from UC Irvine, we wrote a script that trains eight classifiers on characteristics such as clump thickness, uniformity of cell size, uniformity of cell shape, marginal adhesion, single epithelial cell size, bare nuclei, bland chromatin, normal nucleoli, and mitoses. When run on the data, the classifiers were able to achieve up to 96% recall accuracy on a randomly |
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