Feature correlation heatmap. by Ameer Malik Shaik (21263019)

“…Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. …”

Salt spray test setup and sample exposure. by Ameer Malik Shaik (21263019)

“…Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. …”

Model Performance Comparison (dual Y-axis). by Ameer Malik Shaik (21263019)

“…Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. …”

Actual vs. predicted corrosion rates. by Ameer Malik Shaik (21263019)

“…Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. …”

Experimental dataset for corrosion rate analysis. by Ameer Malik Shaik (21263019)

“…Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. …”

Residual analysis of machine learning models. by Ameer Malik Shaik (21263019)

“…Bayesian Ridge regression outperformed other models, achieving an R² of 0.99849 and RMSE of 0.00049, confirming its robustness in capturing corrosion trends. …”

Prediction of peak discharge by Ting Wang (17891264)

“…<p dir="ltr">the original data for '<b>Prediction of peak discharge </b><b>from</b><b> earth-rock dam failures based on Bayesian Optimization XGBoost regression algorithm</b>'</p>…”

Supplementary materials S1-S3 by Yankai Wu (22577300)

“…This file contains Supplementary Materials S1-S3: detailed parameter settings for the base learners and the feature selection procedures (S1); the specific constraints and configurations of the NSGA-II algorithm (S2); and the predefined hyperparameter search ranges used for Bayesian optimization (S3).…”

XGBoost regressor for estimating community-level education percentile rank in China by Yanji Zhang (22363905)

“…<p dir="ltr">We employ an extreme gradient boosting (XGBoost) regressor combined with missing value imputation and Bayesian hyperparameter optimization to train our model and then predict the community’s mean education percentile rank in China. …”

Non-Conjugate Variational Bayes for Pseudo-Likelihood Mixed Effect Models by Cristian Castiglione (21656583)

“…<p>We propose a unified, yet simple to code, non-conjugate variational Bayes algorithm for posterior approximation of generic Bayesian generalized mixed effect models. …”

Data Sheet 1_A novel method for power transformer fault diagnosis considering imbalanced data samples.docx by Jun Chen (4238)

“…Hyperparameter tuning is achieved through the Bayesian optimization algorithm to identify the model parameter set that maximizes test set accuracy.…”

Prediction of New Crystal of Titanium Oxides and Property Calculations Using Structure Prediction Software and First-Principles Theory by Jinrong Huo (10657343)

“…The crystal structure prediction software CBD-GM, based on the Bayesian optimization algorithm combined with deep learning, was used to predict the crystal structure of titanium oxide. …”

Network Varying Coefficient Model by Xinyan Fan (6742421)

“…To estimate the model, we identify the latent “locations” via the latent space model and then develop an iterative projected gradient descent algorithm by optimizing the network parameters and regression coefficients alternately. …”

Posterior Predictive Design for Phase I Clinical Trials by Chenqi Fu (9938465)

“…These designs exhibit robust performance comparable to more intricate, model-based designs, and their pretabulated decision rule enables them to be implemented as simply as the conventional algorithm-based designs. In this paper, we introduce the posterior predictive (PoP) design, a novel interval-based design that leverages advanced Bayesian predictive hypothesis testing techniques for dose escalation and de-escalation. …”

Data Sheet 1_Predictive model for sarcopenia in chronic kidney disease: a nomogram and machine learning approach using CHARLS data.docx by Renjie Lu (2021845)

“…Four machine learning algorithms were utilized, with the optimal model undergoing hyperparameter optimization to evaluate the significance of predictive factors.…”

Table 1_An interpretable machine learning model for predicting mortality risk in adult ICU patients with acute respiratory distress syndrome.docx by Wanyi Li (70701)

“…This study used eight machine learning algorithms to construct predictive models. Recursive feature elimination with cross-validation is used to screen features, and cross-validation-based Bayesian optimization is used to filter the features used to find the optimal combination of hyperparameters for the model. …”

Data Sheet 1_Beyond the current state of just-in-time adaptive interventions in mental health: a qualitative systematic review.pdf by Claire R. van Genugten (20626733)

“…To accomplish this, JITAIs often apply complex analytic techniques, such as machine learning or Bayesian algorithms to real- or near-time data acquired from smartphones and other sensors. …”

Data Sheet 1_MRI-based intra-tumoral ecological diversity features and temporal characteristics for predicting microvascular invasion in hepatocellular carcinoma.docx by Yuli Zeng (20812739)

“…The tumors were segmented into five distinct habitats using case-level clustering and a Gaussian mixture model was used to determine the optimal clusters based on the Bayesian information criterion to produce an iTED feature vector for each patient, which was used to assess intra-tumoral heterogeneity. …”

Image 1_Safety assessment of temozolomidee: real-world adverse event analysis from the FAERS database.png by Yu Liu (6938)

“…Specific detection algorithms also include report Odds ratio (ROR), Proportional Report ratio (PRR), Bayesian confidence propagation neural network (BCPNN), and multi-item Gamma-Poisson constrictor (MGPS).…”

Data Sheet 1_Machine learning-based coronary heart disease diagnosis model for type 2 diabetes patients.docx by Yingxi Chen (599657)

“…</p>Conclusions<p>Based on T2DM data and machine learning theory, a Bayesian-optimized XgBoost model was established using the RFE+LightGBM method. …”