A mean SHAP value report. by Alemu Birara Zemariam (17540938)

“…The performance of the predictive model was evaluated using evaluation metrics value through Python software. …”

A waterfall plot analysis. by Alemu Birara Zemariam (17540938)

“…The performance of the predictive model was evaluated using evaluation metrics value through Python software. …”

DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

“…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”

DataSheet1_Prostruc: an open-source tool for 3D structure prediction using homology modeling.PDF by Shivani V. Pawar (20355171)

“…</p>Methods<p>Prostruc is a Python-based homology modeling tool designed to simplify protein structure prediction through an intuitive, automated pipeline. …”

Submit to AGU-Manuscript-Enhancing Landslide Displacement Prediction Using a Spatio-Temporal Deep Learning Model with Interpretable Features by Jia Wang (20526992)

“…It includes the monitoring data and model prediction results in two Excel files, along with the corresponding Python code used in the study. …”

Data Sheet 1_Machine learning models integrating intracranial artery calcification to predict outcomes of mechanical thrombectomy.pdf by Guangzong Li (16696443)

“…The Extra Trees model demonstrated the highest predictive accuracy. …”

Table 1_Machine learning models for mortality prediction in patients with spontaneous subarachnoid hemorrhage following ICU treatment.docx by Wenwen Hu (403536)

“…</p>Conclusions<p>In our study, the LR model exhibited superior discrimination in predicting risk of mortality among patients with spontaneous SAH compared to other models. …”

Image 1_Machine learning models for mortality prediction in patients with spontaneous subarachnoid hemorrhage following ICU treatment.jpeg by Wenwen Hu (403536)

“…</p>Conclusions<p>In our study, the LR model exhibited superior discrimination in predicting risk of mortality among patients with spontaneous SAH compared to other models. …”

The Twin Prime Conjecture: Collapse-Band Parity Cancellation and Operator Proof by Jennifer Bulyaki (22283308)

Evaluation Metrics for LSTM Model and GRU Model. by Majed Alzara (20700224)

“…This study creates a predictive model just for Egypt’s construction industry that aims to predict a localized CCI to improve financial planning and lower risk. …”

A model of neural population dynamics for flexible sensorimotor control by Hari Kalidindi (17123467)

Comparison of the results obtained combining NeKo and Druglogics pipeline with the original publication from [28]. by Marco Ruscone (11747264)

Table1_Enhanced classification and severity prediction of major depressive disorder using acoustic features and machine learning.pdf by Lijuan Liang (4277053)

“…In addition, we used Python for correlation analysis, and neural network to establish the model to distinguish whether participants experienced depression, predict the total depression score, and evaluate the effectiveness of the classification and prediction model.…”

Supporting data for "Prediction and Classification of Bacterial Virulence Factors using Deep Learning" by Wai Kai Tsui (17853245)

“…The source files contain raw data (i.e., VF and non-VF sequences), processed data, and the model training and validation scripts. The trained model is provided in a compiled standalone python package called DeepVIC. …”

Table 3_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 5_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 1_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 4_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 2_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 3_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”