You've developed a Polynomial Regression model with a high-degree polynomial, and it's performing exceptionally well on the training data but poorly on the test data. What might be the issue, and how would you address it?
- Add more features
- Increase the degree
- Reduce the degree or apply regularization
- Use a different algorithm entirely
The issue likely is overfitting due to the high-degree polynomial. Reducing the degree or applying regularization techniques like Ridge or Lasso can help to reduce the model's complexity and improve generalization to unseen data.
Loading...
Related Quiz
- What is the primary benefit of using transfer learning in deep learning models?
- ____________ Learning, a subset of Machine Learning, is essential in training robots to perform specific tasks in manufacturing industries.
- Deep Q Networks (DQNs) are a combination of Q-learning and what other machine learning approach?
- What are the main challenges in training a Machine Learning model with imbalanced datasets?
- If a machine learning model inadvertently learns societal biases present in its training data, it can result in ________ outcomes.