random forest
PulseAugur coverage of random forest — every cluster mentioning random forest across labs, papers, and developer communities, ranked by signal.
- instance of naive Bayes classifier 90%
- instance of logistic regression model 70%
- used by Shap 70%
- used by DagsHub 70%
- used by alphaXiv 70%
- instance of decision tree 70%
- competes with LightGBM 70%
- instance of multilayer perceptron 70%
- used by decision tree 70%
- competes with Catboost 70%
- instance of Catboost 70%
- competes with convolutional neural network 70%
- 2026-05-19 research_milestone A new paper proposes a kernel-based smoothing mechanism to improve random forest regression. source
22 day(s) with sentiment data
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Machine learning models show improved path loss prediction for LPWANs
Researchers have conducted a systematic analysis of machine learning models for predicting path loss in Low Power Wide Area Networks (LPWANs), specifically focusing on LoRa technology. The study employed Random Forest m…
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IoT Intrusion Detection: Beyond Accuracy to Explanation Cost and Stability
A new study published on arXiv evaluates machine learning models for Internet of Things (IoT) intrusion detection, focusing beyond just accuracy to include explanation cost, stability, and utility. Researchers construct…
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New method optimizes ensemble classifiers for faster predictions
Researchers have developed optimized sequential testing strategies for binary ensemble classifiers, such as random forests. These methods aim to reduce computational costs by evaluating base models sequentially and stop…
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Webcam gaze data fails to improve autonomous driving hazard detection
A new research paper explores whether human gaze data, captured by webcams, can help autonomous driving models avoid developing "mesa-objectives"—internal goals that achieve high training performance through spurious co…
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Machine learning models show accuracy drop with limited residential energy data
A new study published on arXiv compares the effectiveness of various machine learning models for estimating residential energy consumption using limited input data. Researchers found that while models like CatBoost achi…
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New framework optimizes digital twin calibration with budgeted data acquisition
Researchers have developed a new framework for calibrating digital twins, which are virtual replicas of physical systems. This framework addresses the challenge of expensive data collection by optimizing the generation …
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AI framework maps flood and landslide risks with spatial awareness
Researchers have developed a novel framework to map flood and landslide susceptibility and risk across regions like Kerala, India, and Nepal. This framework utilizes a spatial heterogeneity-aware approach, comparing two…
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Machine learning framework detects railway wheel defects using ultrasonic signals
Researchers have developed a machine-learning framework to detect defects in railway wheels using passive ultrasonic signals. The system analyzes acoustic emission data from wheelsets, identifying key features in both t…
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Hybrid AI approach boosts stock prediction accuracy for foundation models
Researchers have developed a hybrid approach to improve the performance of frozen time series foundation models, specifically for high-frequency stock prediction. By combining neural correction architectures like AttnCo…
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New OOD detection framework expands to regression and survival analysis
Researchers have developed a new framework for out-of-distribution (OOD) detection that extends beyond classification to regression and survival analysis. This method is model-aware and subspace-aware, integrating varia…
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New framework aids model selection for sentiment analysis
Researchers have developed a new framework called Cognitive Pairwise Comparison Classification Model Selection (CPC-CMS) to help select the best classification model for document-level sentiment analysis. This framework…
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Research paper highlights limitations of AI explainability for cluster interpretation
A new research paper published on arXiv explores the limitations of current explainability techniques in interpreting clustering results. The study found that methods like Random Forest with permutation feature importan…
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Hybrid ML framework forecasts cattle weight gain in grazing systems
Researchers have developed a hybrid machine learning framework to forecast cattle weight gain and growth patterns in grazing systems. The framework integrates various sensing data, including live weight, demographics, a…
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New AI tool CourseGraph detects course overlaps between universities
Researchers have developed CourseGraph, a new methodology to automatically identify overlapping courses between different universities. This system uses BERT-based language models to semantically analyze course titles, …
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Machine learning framework enhances QKD security against stealthy attacks
Researchers have developed a novel machine learning framework to enhance the detection of eavesdropping attacks in BB84 Quantum Key Distribution (QKD) systems. This framework moves beyond the traditional fixed QBER thre…
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Machine learning models map flood susceptibility in Iran
Researchers have employed advanced machine learning techniques to map flood susceptibility in Iran's Marand Plain. The study utilized five distinct ML algorithms, including Random Forest and Locally Weighted Linear mode…
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ShielDroid framework achieves 97.5% accuracy in Android malware detection
Researchers have developed ShielDroid, a novel framework for detecting Android malware through hybrid dynamic analysis. This approach analyzes application behavior in real-time to identify malicious applications that ev…
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New 'split-candidate scaling' parameter reveals double descent in GBDTs
Researchers have identified a new capacity parameter for gradient boosting decision trees (GBDTs) called split-candidate scaling, which can lead to a phenomenon known as double descent. Unlike neural networks, GBDTs hav…
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New intrusion detection system for medical IoT environments
Researchers have developed a novel intrusion detection system for Internet of Medical Things (IoMT) environments, focusing on feature selection to overcome resource limitations. The system employs a Pearson correlation …
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Tabular foundation models show superior performance in soil spectroscopy
A new research paper explores the effectiveness of tabular foundation models, specifically TabPFN, in soil spectroscopy. The study found that TabPFN consistently outperformed traditional models like CNNs, Random Forests…