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Machine Learning Evaluation| IABAC

Machine learning evaluation measures how well a model performs on specific tasks. It uses various metrics depending on the problem type, such as accuracy for classification or MSE for regression, ensuring the model is reliable, accurate, and fit for deployment.

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Machine Learning Evaluation| IABAC

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  1. Machine Learning Evaluation www.iabac.org

  2. Understanding the Value of ML Evaluation Understanding the value of ML evaluation is essential for ensuring reliable, accurate, and effective machine learning models. It helps identify model performance, uncover biases, and guide improvements. Proper evaluation ensures that models deliver real-world value, make trustworthy predictions, and align with business goals before deployment or scaling. www.iabac.org

  3. A Brief History of ML Evaluation Rule-Based Beginnings: Early AI relied on hand-coded rules with minimal evaluation. Statistical Metrics: Accuracy, precision, and recall became standard in the 1980s–90s. Benchmarking Era: Public datasets and competitions enabled fair model comparisons. Modern Focus: Evaluation now includes fairness, explainability, and robustness. www.iabac.org

  4. Core Evaluation Metrics by Task Classification: Use Accuracy, Precision, Recall, F1 Score, and ROC-AUC to assess how well the model distinguishes between classes. Regression: Key metrics include MAE, MSE, and R² Score to measure prediction error and model fit. Clustering & Ranking: Evaluate with Silhouette Score, ARI (for clustering), and MRR, NDCG, Precision@K (for recommendation systems). www.iabac.org

  5. Popular Validation Methods Train-Test Split K-Fold Cross-Validation Stratified K-Fold Leave-One-Out Bootstrapping www.iabac.org

  6. Beyond Accuracy—Modern Concerns Fairness: Are all groups treated equally? (AI Fairness 360) Explainability: Tools like SHAP, LIME Calibration: Do predictions reflect real-world likelihoods? www.iabac.org

  7. Tools for Evaluation MLflow – Track experiments Evidently AI – Monitor live performance What-If Tool – Visualize decisions TensorBoard – View training metrics Weights & Biases – Log and visualize results www.iabac.org

  8. Trends & Future of ML Evaluation Ongoing monitoring post-deployment Causal evaluation: Why does the model decide that? Simulation testing (e.g., robotics) Human-in-the-loop judgment New legal standards (e.g., EU AI Act) Edge & federated ML: Speed + privacy constraints www.iabac.org

  9. Final Thoughts & Next Steps Evaluation is not a one-time step—it’s continuous Helps ensure trust, fairness, and long-term value Get hands-on with IABAC AI Certification Learn to evaluate models ethically and effectively Prepare for real-world AI applications with confidence www.iabac.org

  10. Thank You www.iabac.org

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