Knowledge Graph — Coursera Notes › Academic disciplines › Computer Science / Information Technology › AI/ML Engineer

Model evaluation and optimization

feature · part of AI/ML Engineer

After development, AI/ML engineers evaluate model performance using metrics like accuracy, precision, recall, F1-score for classification, and mean squared error (MSE) or R-squared for regression. They optimize hyperparameters and fine-tune architecture to maximize performance. For example, in finance, they build fraud detection algorithms and use these metrics to improve accuracy in identifying suspicious transactions.

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