How can the impact of outliers on a machine learning model be mitigated? a) applying data augmentation techniques b) using robust scaling methods c) implementing recursive feature... How can the impact of outliers on a machine learning model be mitigated? a) applying data augmentation techniques b) using robust scaling methods c) implementing recursive feature elimination d) applying frequency encoding
Understand the Problem
The question is asking about different methods to mitigate the impact of outliers on a machine learning model. It presents four options and seeks to identify the appropriate strategies to handle outliers effectively.
Answer
Using robust scaling methods.
The impact of outliers on a machine learning model can be mitigated by using robust scaling methods.
Answer for screen readers
The impact of outliers on a machine learning model can be mitigated by using robust scaling methods.
More Information
Robust scaling methods help in mitigating the effect of outliers by using techniques that are not affected by extreme values, such as using the median and interquartile range for scaling.
Tips
A common mistake is to assume all scaling methods handle outliers. Only robust scaling is specifically designed to handle them effectively.
Sources
- Handling Outliers in ML: Best Practices for Robust Data Preprocessing - linkedin.com
- How to Scale Data With Outliers for Machine Learning - machinelearningmastery.com
