Predictive variables screened by LASSO regression and Random Forest algorithm. by Da-cheng Wang (22232009)

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Metrics obtained for different regression algorithms and target variables. by Silverio García-Cortés (4746477)

Metrics obtained for different regression algorithms after Hyperparamter Optimization. by Silverio García-Cortés (4746477)

Architecture of the EfficientNetB3-based regression model used for cattle weight prediction. by Md Junayed Hossain (22615268)

Top features identified by the stepwise additive feature selection algorithm. by Kylie K. Hornaday (9713558)

The net reclassification index and integrated discrimination improvement analysis and Learning curves of four models. by Da-cheng Wang (22232009)

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Monthly Distribution of Global Horizontal Irradiance (GHI) in Yulin Region (Data Source: https://energyplus.net/weather-search/yulin). by Juan Ren (733592)

Validation Loss vs. Training Loss Curves for the CNN architectures and EfficientNet B3 model evaluated in this study. by Md Junayed Hossain (22615268)

Data Sheet 1_Optimization of clustering parameters for single-cell RNA analysis using intrinsic goodness metrics.pdf by Nicolina Sciaraffa (19329285)

“…Consequently, fifteen intrinsic measures have been calculated and used to train an ElasticNet regression model in both intra- and cross-dataset approaches to evaluate the possibility of predicting the clustering accuracy.…”

Table 2_An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development... by Landan Kang (20108214)

“…Multiple machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF), and Naive Bayes (NB), were applied to construct predictive models. …”

Table 1_An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development... by Landan Kang (20108214)

“…Multiple machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF), and Naive Bayes (NB), were applied to construct predictive models. …”

Decision curve analysis of age and the full model. by Da-cheng Wang (22232009)

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Pearson correlation matrix of clinical variables. by Da-cheng Wang (22232009)

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SHAP-based interpretation for the Random Forest model. by Da-cheng Wang (22232009)

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Metrics of four models with fivefold cross-validation repeated 100 times in training cohort. by Da-cheng Wang (22232009)

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Confusion matrix and calibration curves of four models in validation cohort. by Da-cheng Wang (22232009)

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Receiver operator characteristic (ROC) curves of five independent predictive variables for predicting NOAF. by Da-cheng Wang (22232009)

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Baseline characteristics of patients in training and validation groups. by Da-cheng Wang (22232009)

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Receiver operator characteristic curve, Precision-Recall curve and decision curve analysis of four models in validation cohort. by Da-cheng Wang (22232009)

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The performance of different regression models. by Zeonlung Pun (21395508)