Sure, here are five questions on Supervised Machine Learning (ML):
1. **What is Supervised Learning in ML?**
- This question seeks an explanation of the basic concept of supervised learning, including
how it works and its key characteristics.
2. **How does a Supervised Learning algorithm differ from an Unsupervised Learning
algorithm?**
- This question aims to understand the fundamental differences between supervised and
unsupervised learning, focusing on their approaches to learning from data.
3. **Can you provide examples of real-world applications of Supervised Learning?**
- This question asks for practical examples where supervised learning is applied, helping to
illustrate its usefulness and versatility in various industries.
4. **What are the typical steps involved in building a Supervised Learning model?**
- Here, the focus is on the process of developing a supervised learning model, from data
collection and preprocessing to training and evaluation.
5. **What are some common challenges faced when implementing Supervised Learning
models, and how can they be addressed?**
- This question delves into the difficulties practitioners might encounter when working with
supervised learning, such as overfitting, underfitting, and data quality issues, and seeks
solutions or best practices to mitigate these challenges.
