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Mar 04 2024

ML Model Evaluation

This notebook showcases a performance evaluation pipeline for a binary classification model. It begins by generating predictions from probability scores using a customizable threshold and then calculates key evaluation metrics: accuracy, error rate, precision, recall, F1 score, true positive rate (TPR), and false positive rate (FPR). These metrics offer a well-rounded view of model performance, especially in imbalanced or cost-sensitive scenarios.

The notebook concludes with the generation of a Receiver Operating Characteristic (ROC) curve using the scikit-learn library, which provides a visual diagnostic of how well the model distinguishes between the two classes. The pipeline is modular, interpretable, and suitable for iterative threshold tuning, making it a valuable asset in any classification project where explainability and evaluation rigor are priorities.

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