The dataset in the image shows two distinct groups of points: one class represented by green points and another class represented by red points. The two groups are mostly separated, with the green points predominantly on the left side and the red points predominantly on the right side of the graph.

However, there are two points that appear to be in the wrong region:

• One green point is located within the cluster of red points on the right side.
• One red point is located within the cluster of green points on the left side.

These two points are outliers because they are in the region of the opposite class. Therefore, the total number of outliers in the dataset is:

2

Would you like further explanation on this?

Here are some follow-up questions that expand on this topic:

1. What defines an outlier in a supervised learning context?
2. How do different machine learning algorithms handle outliers?
3. What are some common techniques to remove or handle outliers in data preprocessing?
4. How can outliers affect the accuracy of a classification model?
5. What role do outliers play in unsupervised learning models like clustering?

Tip: When dealing with outliers in a dataset, consider using robust methods like median or interquartile ranges to mitigate their influence on the model.