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What is Random Forests

Chapter 22
Handbook of Research on Cyber Approaches to Public Administration and Social Policy
There seem to be various random forests in this classifier that offer a value. The matter to the most votes is the actual outcome—Researchers in employed several machine learning classifiers to detect false news. The random forest is also one of those classifiers.
Published in Chapter:
An Analysis and Detection of Misleading Information on Social Media Using Machine Learning Techniques
Ritushree Narayan (Usha Martin University, Ranchi, India), Keshav Sinha (Sarala Birla University, India), and Devesh K. Upadhyay (Birla Institute of Technology, Mesra, India)
Copyright: © 2022 |Pages: 22
DOI: 10.4018/978-1-6684-3380-5.ch022
Abstract
The widespread adoption of user-generated material on social networking sites enables the gathering of individuals. The internet has grown in popularity based on multidisciplinary information sources. Nowadays, every individual has constantly bombarded the internet with information, and it is very challenging for every person to distinguish between factual and misleading information. Social networking sites mainly rely on content providers to filter the information. The chapter has focused on political news where the machine learning-based hybrid approach has been used to detect false statements. The work is to determine the information is deceptive or accurate. The authors investigate the link between publisher attitude and news stance, and the hyperpartisan media sources are more prone than other resources to propagate false information. Furthermore, they show that this is not required to examine news and information to recognize misleading headlines, but that utilizing variables such as publisher bias, user interactions, and news-related pictures may obtain equivalent results.
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Chapter 17
Comparing Conventional Methods With Fuzzy Logic for Quantifying Road Congestion: Evidence From Central Kolkata, India
There seem to be various random forests in this classifier that offer a value. The matter of the most votes is the actual outcome—Researchers employed several machine learning classifiers to detect false news. The random forest is also one of those classifiers.
Published in Chapter: Comparing Conventional Methods With Fuzzy Logic for Quantifying Road Congestion: Evidence From Central Kolkata, India; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
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Chapter 8
Deep Learning for Facial Skin Issues Detection: A Study for Global Care With Healthcare 5.0
Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees.
Published in Chapter: Deep Learning for Facial Skin Issues Detection: A Study for Global Care With Healthcare 5.0; From: Federated Learning and AI for Healthcare 5.0
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Chapter 187
Identifying Disease and Diagnosis in Females Using Machine Learning
It known as random decision forests is an ensemble learning approach for classification, regression, and other tasks that works by building a large number of decision trees during training. For classification tasks, the random forest's output is the class chosen by the majority of trees.
Published in Chapter: Identifying Disease and Diagnosis in Females Using Machine Learning; From: Encyclopedia of Data Science and Machine Learning
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Chapter 6
Holistic View on Detecting DDoS Attacks Using Machine Learning
Supervised method used for classification and regression problems, that builds decision trees on different samples and takes their average in case of regression and majority vote for classification.
Published in Chapter: Holistic View on Detecting DDoS Attacks Using Machine Learning; From: Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity
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Chapter 18
Skin Cancer Lesion Detection Using Improved CNN Techniques
The random forest offers the value, and the votes are used for the actual outcome. The classifiers used to detect false news.
Published in Chapter: Skin Cancer Lesion Detection Using Improved CNN Techniques; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
Full Text Chapter Download: US $37.50 Add to Cart
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