10.8: Assessments
- Page ID
- 58334
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Learning Objectives
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Define predictive analytics and explain its relationship to descriptive and prescriptive analytics.
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Distinguish between supervised and unsupervised learning and identify appropriate problem types for each.
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Describe common ML algorithms: linear regression, logistic regression, decision trees, random forests, and K-means clustering.
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Evaluate model performance using metrics: accuracy, precision, recall, F1 score, and confusion matrices.
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Explain overfitting and underfitting and strategies to prevent them (train/test split, cross-validation, regularization).
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Implement basic predictive models using Python's scikit-learn library.