random forest
PulseAugur coverage of random forest — every cluster mentioning random forest across labs, papers, and developer communities, ranked by signal.
- instance of decision tree 90%
- instance of DagsHub 90%
- used by principal component analysis 90%
- competes with TabPFN 80%
- instance of logistic regression model 70%
- used by DagsHub 70%
- instance of multilayer perceptron 70%
- used by ScienceCast 70%
- used by alphaXiv 70%
- used by Gotit.pub 70%
- competes with multilayer perceptron 70%
- competes with LightGBM 70%
- 2026-05-19 research_milestone A new paper proposes a kernel-based smoothing mechanism to improve random forest regression. source
15 day(s) with sentiment data
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MouldIt model boosts data efficiency in industrial process learning · 1 source tracked
Researchers have developed MouldIt, a novel variational autoencoder model that utilizes temporal convolution and attention mechanisms for enhanced data efficiency in industrial processes. This model is designed to under…
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New Corpus MISO-BR Targets Misogyny Detection in Brazilian Portuguese
Researchers have introduced MISO-BR, a new open corpus designed for detecting misogyny in Brazilian Portuguese social media text. The corpus contains 896 annotated social media posts, developed with a detailed construct…
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Machine learning model achieves 96% accuracy in phishing website detection
A step-by-step guide details how to build a phishing website detector using PySpark and machine learning. The project utilized a dataset of 11,430 URLs, analyzing 89 features to identify malicious sites. The best-perfor…
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New AI model identifies optimal global solar power plant locations
Researchers have developed a new method for identifying optimal locations for solar power plants using geospatial embeddings. This approach analyzes existing solar sites to learn a suitability signal, outperforming trad…
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New dataset AraEngage analyzes Arabic Facebook news engagement
Researchers have developed AraEngage, a dataset of 10,000 Arabic Facebook news posts, to analyze linguistic features associated with user engagement. The study found that religious expressions, message length, sentence …
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Deep learning and SHAP analysis enhance MRI brain tumor detection
Researchers have developed an enhanced method for detecting brain tumors using Magnetic Resonance Imaging (MRI) combined with deep learning and explainable AI. The study explored various feature representation schemes, …
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New audit method reveals prediction model miscalibrations and demographic disparities
A new auditing method has been developed to identify where prediction models are miscalibrated and for whom, particularly concerning demographic disparities. This approach uses random forests to group similar observatio…
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New FLAT method improves sample-efficient traffic calibration for digital twins
Researchers have developed FLAT, a novel method for sample-efficient traffic calibration in digital twins. This approach smooths the parameter-error landscape, making the objective learnable from a limited number of sam…
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New VitisGP dataset benchmarks machine learning for grapevine trait prediction
Researchers have introduced VitisGP, a new multi-phenotype dataset designed to predict grapevine characteristics from genomic data. This dataset integrates genome-wide SNP data with 26 different prediction tasks, includ…
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LLMs evaluated for molecular property prediction, showing mixed results on accuracy and uncertainty
Researchers are exploring the use of large language models (LLMs) for molecular property prediction, a field previously dominated by classical methods. A study evaluating seven frontier LLMs found that while they can im…
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Data augmentation boosts sentiment classification on small datasets
A new arXiv paper explores methods to improve sentiment classification performance on small datasets. The study compares three data augmentation techniques: Easy Data Augmentation (EDA), back-translation, and contextual…
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Multi-agent systems should avoid averaging, favor evidence-based challenges
A recent analysis argues against averaging or voting mechanisms in multi-agent systems, stating that such methods destroy valuable disagreement among agents. Instead of combining outputs, the author proposes a challenge…
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New hybrid AI framework boosts accuracy for solving complex differential equations
Researchers have developed a new hybrid framework that combines Physics-Informed Neural Networks (PINNs) with boosting methods to solve complex differential equations. This approach enhances the accuracy of standard PIN…
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New LSTM-beta-VAE framework enhances AI for industrial anaerobic digestion
Researchers have developed a novel generative data augmentation framework using an LSTM-beta-Variational Autoencoder combined with a multi-output LSTM (MO-LSTM) soft sensor. This approach addresses data sparsity and the…
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New study questions LLM causal reasoning benchmarks
A new research paper published on arXiv questions the validity of the CLadder benchmark for evaluating causal reasoning in large language models (LLMs). The study, conducted by Cladder & Jansen, found that superficial p…
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AI model flags satellite collision risks using Random Forest and XGBoost
A data scientist has developed a supervised machine learning pipeline to identify potential satellite collisions. The system uses Random Forest and XGBoost models trained on synthetic data to flag close approaches with …
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AI framework enhances fraud detection in digital banking
This study introduces a novel framework for detecting fraud in digital banking, particularly addressing challenges like imbalanced data and operational limits. The framework incorporates chronological evaluation, featur…
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Low-cost AgriDefend device uses AI to detect crop stress before symptoms appear
Researchers have developed AgriDefend, a low-cost offline edge device designed to detect crop stress before visible symptoms appear. This device fuses data from canopy imagery analyzed by a MobileNetV2 neural network an…
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AI model enhances type 2 diabetes diagnosis accuracy
Researchers have developed a new model that combines the Random Forest classifier with a Bat Optimization Algorithm to improve the accuracy of type 2 diabetes diagnosis. By first addressing class imbalance and noise in …
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Boruta gene selection method performance impacted by tree depth
A new research paper explores the impact of tree depth settings in the Boruta gene selection method for RNA-Seq data classification. The study found that restricting tree depth, a common practice, significantly alters g…