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 in machine learning refer to the process of assessing how well a model performs on a given task using quantitative measures. This involves applying specific metrics—such as accuracy, precision, recall, F1-score for classification tasks, and mean squared error or R² for regression—to determine how closely the model’s predictions align with actual outcomes. Evaluation is typically conducted using separate validation or test datasets to ensure that the model generalizes well to unseen data rather than simply memorizing training data. Proper evaluation helps in comparing different models, tuning hyperparameters, detecting issues like overfitting or underfitting, and ultimately selecting the most reliable model for deployment in real-world applications.
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.
