https://file.aiquickdraw.com/mathbot/file/17264875597978tvvyewk.jpg

To determine the number of outliers in the dataset, we need to examine the green and red points in the scatter plot. The goal is to identify points that do not belong to the cluster of their own class.

- The red points and green points belong to two different classes.
- The data appears to form two distinct clusters: 
  - One cluster has mostly red points on the right.
  - The other cluster has mostly green points on the left.

### Analysis:
- **Left Cluster (mostly green points):** 
  - There are two red points mixed within the green cluster.
  
- **Right Cluster (mostly red points):**
  - There is one green point mixed within the red cluster.

These points are outliers because they are located within a region dominated by the other class.

### Conclusion:
- **Outliers:** 
  - Two red points in the green cluster.
  - One green point in the red cluster.
  
Thus, there are **3 outliers** in total.

Would you like any further details or have any questions?

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Here are 5 related questions you might want to explore:
1. What defines an outlier in machine learning datasets?
2. How do outliers affect the performance of supervised learning models?
3. What techniques can be used to detect outliers in larger datasets?
4. How do different clustering algorithms handle outliers?
5. What impact do outliers have on decision boundaries in classification problems?

**Tip:** Always visualize your dataset during the initial stages of model building to easily spot any outliers or inconsistencies.

Consider the following dataset for a supervised learning problem. Green and red points belong to two different classes. How many outliers does the dataset have?

Explore how to determine outliers in a supervised learning dataset with red and green points representing two different classes. Identify how outliers are points that do not belong to their cluster. This exercise is suited for machine learning beginners and focuses on classification and data visualization.

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