Pros and Cons of Parametric models

Some examples of parametric machine learning algorithms include:

  • Logistic Regression
  • Linear Discriminant Analysis
  • Perceptron
  • Naive Bayes
  • Simple Neural Networks

Benefits of Parametric Machine Learning Algorithms:

  1. Simpler: These methods are easier to understand and interpret results.
  2. Speed: Parametric models are very fast to learn from data.
  3. 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:

  1. Constrained: By choosing a functional form these methods are highly constrained to the specified form.
  2. Limited Complexity: The methods are more suited to simpler problems.
  3. Poor Fit: In practice the methods are unlikely to match the underlying mapping function.