Some examples of parametric machine learning algorithms include:
- Logistic Regression
- Linear Discriminant Analysis
- Naive Bayes
- Simple Neural Networks
Benefits of Parametric Machine Learning Algorithms:
- Simpler: These methods are easier to understand and interpret results.
- Speed: Parametric models are very fast to learn from data.
- Less Data: They do not require as much training data and can work well even if the fit to the data is not perfect.
Limitations of Parametric Machine Learning Algorithms:
- Constrained: By choosing a functional form these methods are highly constrained to the specified form.
- Limited Complexity: The methods are more suited to simpler problems.
- Poor Fit: In practice the methods are unlikely to match the underlying mapping function.