Removing outliers from a dataset has various advantages. For starters, it improves the precision and reliability of statistical studies by eliminating the distortion created by extreme numbers. This produces more robust and reliable outcomes, which improves the validity of data-driven conclusions. Additionally, outlier elimination can reduce the impact of anomalies on model performance, resulting in improved forecast accuracy and generalization to new data. Furthermore, it simplifies data interpretation by emphasizing on the variables’ central tendency and normal behavior, making it simpler to spot patterns and trends. Overall, outlier elimination helps to provide more useful and trustworthy data analysis results.
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