Certainly! Here are five additional questions on Supervised Machine Learning:
1. **What are the differences between classification and regression in Supervised Learning?**
- This question explores the distinction between the two primary types of tasks in supervised
learning: classification (predicting discrete labels) and regression (predicting continuous
outputs).
2. **How do you evaluate the performance of a Supervised Learning model?**
- This question seeks to understand the various metrics and methods used for assessing the
accuracy and effectiveness of a supervised learning model.
3. **What role does feature selection play in Supervised Learning, and what are some common
techniques?**
- This question focuses on the importance of choosing the right features (input variables) for a
model, and asks for examples of techniques used in feature selection and reduction.
4. **Can you explain the concept of overfitting in Supervised Learning, and how it can be
prevented?**
- This question addresses one of the key challenges in machine learning, where a model
performs well on training data but poorly on unseen data, and seeks strategies for avoiding this
issue.
5. **What is the importance of cross-validation in Supervised Learning, and how is it typically
implemented?**
- This question is about the cross-validation technique used to ensure that a supervised
learning model generalizes well to new data, asking for an explanation of its importance and
common methods of implementation.
