Regression-Based Learning Models
Regression models are essential for predicting continuous outcomes across scientific and business domains. It examines methods such as linear regression, polynomial regression, and regularization techniques like L1/L2 penalties. Learners evaluate model fit using metrics including mean squared error and explore optimization through gradient descent. Practical applications include forecasting, cost estimation, and environmental prediction. This will also discuss assumptions and limitations underlying regression techniques. It develops analytical and modeling expertise for quantitative prediction.
Modeling Components:
- Regression algorithms and variations
- Error metrics and evaluation
- Regularization strategies
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