Machine Learning: Random Forest with Python from Scratch - Outliers

The video tutorial discusses data and models, focusing on outliers in datasets. It explains what outliers are, how they can occur due to human error or faulty equipment, and provides examples such as age and temperature anomalies. The tutorial emphasizes the importance of detecting and removing outliers using tools and visualization techniques. It concludes with a preview of the next lecture on machine learning models.

What steps should be taken to handle outliers in a data set?

What is the significance of removing outliers from a data set?