Advanced Supervised Learning & Evaluation (Weeks 8)
The module advances students' understanding of supervised learning, focusing on model evaluation and hyperparameter optimization. Key techniques covered include the Receiver Operating Characteristic (ROC) Curve, Area Under the Curve (AUC), and the Precision-Recall Curve, particularly in scenarios involving imbalanced datasets. Furthermore, the chapter addresses hyperparameter tuning strategies via Grid Search and Random Search, along with diagnostic tools like Learning Curves and Validation Curves to enhance model performance evaluation.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Advanced evaluation metrics are crucial for understanding classifier performance in imbalanced datasets.
- Hyperparameter optimization is essential for maximizing the effectiveness and generalization power of machine learning models.
- Learning Curves and Validation Curves serve as crucial diagnostic tools for identifying bias-variance trade-offs and improving model complexity.
Key Concepts
- -- ROC Curve
- A graphical representation that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold varies, plotting True Positive Rate against False Positive Rate.
- -- AUC
- The Area Under the ROC Curve summarizes the overall performance of a classifier, representing the probability that the classifier ranks a randomly chosen positive instance higher than a randomly chosen negative instance.
- -- PrecisionRecall Curve
- A plot that focuses on the performance of a classifier on the positive class, highlighting the trade-off between precision and recall, especially important in imbalanced datasets.
- -- Hyperparameter Optimization
- The systematic process of finding the optimal combination of external configurations (hyperparameters) of a machine learning algorithm to improve performance.
- -- Learning Curves
- Graphs that show a model's learning performance over varying sizes of training datasets, helping diagnose high bias or high variance.
- -- Validation Curves
- Graphical representations that show how the performance of a machine learning model changes as a specific hyperparameter is varied.
Additional Learning Materials
Supplementary resources to enhance your learning experience.