Classification Algorithms and Decision Systems
Classification methods categorize data into meaningful groups, supporting applications such as disease diagnosis and fraud detection. It explores algorithms including logistic regression, support vector machines, decision trees, and k-nearest neighbors. Students analyze model performance using precision, recall, and the confusion matrix. The role of classification thresholds and data imbalance is highlighted. Real-world case studies demonstrate the importance of robust classification systems. It equips learners with essential predictive modeling skills.
Classification Elements:
- Algorithmic approaches to categorization
- Performance evaluation metrics
- Handling imbalanced datasets
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