Nigeria's free digital learning hub. Videos, lecture notes, practice questions and leaderboards aligned with CCMAS, NBTE, NCCE, WAEC, NECO, IJMB and JAMB curricula.
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Nigeria's free digital learning hub. Videos, lecture notes, practice questions and leaderboards aligned with CCMAS, NBTE, NCCE, WAEC, NECO, IJMB and JAMB curricula.
Model evaluation and performance metrics refer to the methods used to assess how well a machine learning or statistical model performs in capturing patterns and making accurate predictions or inferences. These metrics vary depending on the task—for example, classification models are often evaluated using accuracy, precision, recall, F1-score, and ROC-AUC, while regression models use measures such as mean squared error (MSE), mean absolute error (MAE), and R-squared. For unsupervised tasks like clustering or topic modeling, metrics such as coherence score, perplexity, and silhouette score are commonly used to evaluate the quality and interpretability of the results. Effective model evaluation involves not only selecting appropriate metrics but also validating performance using techniques like cross-validation to ensure the model generalizes well to unseen data, thereby preventing issues such as overfitting or underfitting.
This lecture is part of CSC316 MACHINE LEARNING in the Computer Science department. The course has 30 recorded lectures across 10 series, all free to watch.
