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Generate realistic test data in Python fast. No dataset required

Learn the NumPy trick for generating synthetic data that actually behaves like real data.
IEEE

Random Forest Regression for Improving the Measurement Range of a Temperature Interferometric Sensor

Abstract: In this work, a random forest regression was used to predict the temperature of an interferometric optical sensor over a wide measurement range, overcoming several times the $2\pi $ ...
GitHub

Quantile-Based Prediction using Regression Trees and Forests

Quantile regression forest consistently achieves the lowest pinball loss and effect of split criterion on QRF is minimal. This shows the advantage instability and generalization over single-tree ...
GitHub

Linear Regression Practice Using Python

From the first 5 rows of the dataset, we can see that there are several columns available: species, island, bill_length_mm, bill_depth_mm, flipper_length_mm, body_mass_g, and sex. There also appears ...
IEEE

Energy-Efficient Multi-Stream Sparse Regression RaptorQ Codes for Unsourced Random Access

Abstract: In the paper, we propose a novel RaptorQ-based unsourced random access (URA) scheme that integrates RaptorQ codes and sparse regression codes (SPARCs) to design access schemes tailored for ...
Frontiers

Prediction of total iodine dose of I-131 therapy for Graves’ hyperthyroidism achieved remission status: a random forest regressor model approach to assess treatment efficacy

Department of Nuclear Medicine, The People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi Zhuang Autonomous Region, China Objective: Graves’ hyperthyroidism (GH) presents significant ...