What is advanced outlier removal in data analysis?

What is advanced outlier removal in data analysis?

Advanced outlier removal refers to sophisticated techniques used to identify and eliminate outliers from datasets, improving data quality.

Why is it important to remove outliers?

Removing outliers is crucial as they can skew results, lead to incorrect conclusions, and affect the performance of machine learning models.

What are some common methods for outlier removal?

Common methods include Z-score analysis, IQR (Interquartile Range), and robust statistical techniques like the Median Absolute Deviation.

Can you explain the Z-score method?

The Z-score method calculates how many standard deviations an element is from the mean. If the Z-score is above a certain threshold, it's considered an outlier.

Are there any risks in removing outliers?

Yes, removing outliers can sometimes lead to loss of valuable information, especially if those outliers are genuine data points.