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Dealing with Outliers and Anomalies
Автор: NextGen AI Explorer
Загружено: 2025-07-30
Просмотров: 29
Описание: Outliers and anomalies can skew data analysis results and lead to incorrect model predictions. Identifying them is the first step, often done through statistical methods such as the Z-score or IQR. Once identified, you can choose to transform them to reduce their impact or remove them if they result from errors. However, it's crucial to understand their origin, as some outliers might provide valuable insights. During this segment, we'll explore techniques to manage outliers and discuss their impact on model performance, supported by a real-world example.
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