The performance of the model is quantified vs the robustness of the prediction model was

Examples:

• The performance of the model is quantified using various metrics such as accuracy and precision.
• In order to improve the model, the performance needs to be quantified accurately.
• Quantifying the performance of the model helps in assessing its effectiveness.
• Researchers often quantify the performance of their models to compare different approaches.
• The quantification of the model's performance provides valuable insights for further optimization.

Alternatives:

• The model's performance is measured.
• The performance of the model is evaluated.
• The model's performance is assessed quantitatively.
• Performance metrics are used to quantify the model's performance.
• Quantitative evaluation of the model's performance is essential.

Examples:

• The robustness of the prediction model was tested under various scenarios.
• Assessing the robustness of the prediction model is crucial for its reliability.
• The study focused on improving the robustness of the prediction model.
• Researchers aim to enhance the robustness of their prediction models for real-world applications.
• The robustness of the prediction model determines its effectiveness in diverse situations.

Alternatives:

• The prediction model's robustness was evaluated.
• The robustness of the model for prediction was examined.
• Assessing the resilience of the prediction model is important.
• The prediction model's ability to withstand challenges was tested.
• Evaluating the robustness of the prediction model is a key step in model development.