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Model Evaluation Metrics Explained by sanket thapliyal is a document available to read on EtoBox.

What is Model Evaluation Metrics Explained about?

The document outlines essential metrics for evaluating model performance, including classification metrics such as accuracy, precision, recall, F1-score, and ROC-AUC, as well as regression metrics like Mean Absolute Error, Mean Squared Error, and R² Score. It also provides a Python example demonstrating how to calculate accuracy using the sklearn library. Model evaluation is crucial for understanding the effectiveness of predictive models.

Author

sanket thapliyal

Language

EN