Certificate in Model Evaluation for Supply Chain
-- ViewingNowThe Certificate in Model Evaluation for Supply Chain is a comprehensive course designed to equip learners with essential skills in supply chain data analysis and model evaluation. This program emphasizes the importance of data-driven decision-making in supply chain management and provides hands-on experience with industry-standard tools and techniques.
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โข Introduction to Model Evaluation – Understanding the basics of model evaluation, its importance, and the common metrics used in assessing the performance of supply chain models. โข Data Preprocessing – Data cleaning, normalization, and transformation techniques to ensure the accuracy and reliability of the data used in model evaluation. โข Performance Metrics for Regression Models – Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), R-squared, Adjusted R-squared, and other metrics used in evaluating regression models. โข Performance Metrics for Classification Models – Accuracy, Precision, Recall, F1-score, Confusion Matrix, and other metrics used in evaluating classification models. โข Cross-Validation Techniques – K-fold cross-validation, stratified k-fold cross-validation, leave-one-out cross-validation, and other techniques to ensure the robustness and generalizability of the model. โข Bias-Variance Tradeoff – Understanding the concept of bias and variance in model evaluation and learning how to balance the two to optimize model performance. โข Overfitting and Underfitting – Recognizing the signs of overfitting and underfitting in supply chain models and applying techniques to mitigate these issues. โข Model Selection and Evaluation Tools – Using various tools and libraries, such as scikit-learn, to streamline the model evaluation process.
โข Case Studies in Supply Chain Model Evaluation – Analyzing real-world examples of supply chain model evaluation and learning how to apply the concepts and techniques covered in the course.
Note: The above list of units is not exhaustive and can be tailored to the specific needs and requirements of the course.
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