Detecting and handling outliers
Outliers are data points that significantly deviate from the general pattern or trend shown by most of the data points in a dataset. They lie at an unusually distant location from the center of the data distribution and can have a significant impact on statistical analyses, visualizations, and model performance. Defining outliers involves recognizing data points that do not conform to the expected behavior of the data and understanding the context in which they occur.
Impact of outliers
Outliers, while often a small fraction of a dataset, wield a disproportionate influence that can disrupt the integrity of a dataset. Their presence has the potential to distort statistical summaries, mislead visualizations, and negatively impact the performance of models.
Let’s go deeper into the various ways in which outliers distort the truth:
- Distorted summary statistics: Outliers can significantly skew summary statistics, giving a misleading...
