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Classification Modeling and Evaluation
A classification module focused on supervised decision models, feature preparation, and evaluation across applied binary and multi-class tasks.
Months 11-12Predictive Modeling SpecializationModule 07 of 11
Why This Module Matters
It complements regression work by expanding the learner's toolkit for detection, filtering, sentiment, fraud, and other label-based problems.
Detailed Module Breakdown
- Classification problem framing and target-label preparation
- Common supervised methods and their practical tradeoffs
- Feature engineering, validation, and decision-threshold thinking
- Performance analysis using confusion matrices and classification metrics
What You Will Study
- KNN, SVM, tree-based, logistic, and probabilistic classification methods
- Feature engineering for decision-oriented modeling tasks
- Metric interpretation with precision, recall, F1, and ROC-AUC
Outcomes You Carry Forward
- Build classification pipelines with stronger evaluation habits
- Interpret decision-model metrics in practical settings
- Select methods with better alignment to the target problem
Module Details
Requirements
- Machine learning foundation and familiarity with supervised workflows
- Comfort with data preparation and validation steps
Best Suited For
- Students specializing in decision-oriented machine learning tasks
- Learners preparing for fraud, risk, text, or behavioral classification work
Delivery Notes
- Assessment emphasizes metric interpretation, not just model execution
- Students are expected to justify modeling choices against task goals
Phase Skills
This phase deepens applied machine learning through regression and classification, with emphasis on feature engineering, model comparison, and metrics such as precision, recall, F1, and ROC-AUC.
Problem framing, model selection, and metric interpretationPrecision, recall, ROC-AUC, and comparative validation practice
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