Feature Importance Analysis: Extracted and visualized feature importances from the Random Forest and XGBoost models to understand key drivers of churn. Conclusion on Models: While Logistic Regression ...

In univariate logistic regression (stepwise method), variables with p ... Software packages used include glmnet (LASSO), rms (RCS), xgboost, lightgbm, scikit-learn (machine learning), survminer ...

Machin Learning Full Algorithm (Linear Regression, Decision tree, Random forest, Neural network ,Logistic regression ,Support vector machine ,Naive Bayes ,Clustering ...

Here, regression algorithms are used to predict future TL CO with SHapley Additive exPlanations (SHAP) employed to improve explainability. Linear, random forest (RF) and XGBoost regression algorithms ...

Results: Comparing two regression machine learning models [Random Forest (RF) and XGBoost], we found that facial emotional expressions were promising indicators of stress scores, with model fit being ...