Random Forest also known as random decision forests are a specific type of ensembling algorithm that utilizes a combination of decision trees based on subsets of a dataset. A random forest algorithm does not make a decision tree of smaller decision trees, but rather utilizes decision trees in parallel for prediction. Random forest algorithms are typically more accurate than single decision trees although less intuitive.
A specific type of random forest algorithm that utilized bagging was originally coded into a software package and registered with a trademark under the name Random Forests by Leo Breiman and Adele Cutler 1.
Random forest algorithms have been used for classification tasks including diagnostic, prognostic 2 and segmentation tasks in radiology. Random forest algorithms can also be applied to regression and other statistical problems.
- 1. Minitab, Inc. "U.S. trademark registration number 3185828, registered 2006/12/19", 2006.
- 2. Manisha Bahl, Regina Barzilay, Adam B. Yedidia, Nicholas J. Locascio, Lili Yu, Constance D. Lehman. High-Risk Breast Lesions: A Machine Learning Model to Predict Pathologic Upgrade and Reduce Unnecessary Surgical Excision. (2017) Radiology. 286 (3): 810-818. doi:10.1148/radiol.2017170549 - Pubmed
- 3. Cha Zhang, Yunqian Ma. Ensemble Machine Learning. (2012) ISBN: 9781441993250
Related Radiopaedia articles
- artificial intelligence (AI)
- imaging data sets
- computer-aided diagnosis (CAD)
- machine learning (overview)
- common data preparation/preprocessing steps
- DICOM to bitmap conversion
- principal component analysis
- training, testing and validation datasets
- mean squared error
- cross entropy
- optimization algorithms
- stochastic gradient descent
- momentum (Nesterov)
- linear and quadratic
- batch normalization
- natural language processing
- rule-based expert systems