A base model is created on this subset ... customers or Retail channel (nominal) customers. I implement XGBoost with Python and Scikit-Learn to solve the classification problem. I have used the ...

This involves splitting the dataset into a test set and a training set. Then we train the XGBoost model with XGBRegressor and make a prediction with the fit method. Finally, we use MAE (mean ...

Examples of techniques for training interpretable machine learning (ML) models, explaining ML models, and debugging ML models for accuracy, discrimination, and security. Usage of artificial ...

Open-source libraries in Python and R were used in the analyses ... Subsequent Kaplan-Meier plots supported the finding of the XGBoost model. The computation of SHapley Additive exPlanation values, ...

Additionally, the XGBoost-SMOTE model can balance the uneven proportion of high-pollution and normal samples, significantly improving the optimization performance of the numerical model at high ...

For instance, in a CPG demand forecasting project, our XGBoost model outperformed traditional ... performance progressively. I once used Python's strength to strengthen an AdaBoost-based fraud ...

In this study, Binomial Logistic Regression model was built to get a multivariate linear relationship between dependent variable and independent variables and XGBoost model was set to get a ...