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Related Questions
- What are some common methods for identifying and handling outliers in a dataset, such as the density-based spatial clustering of applications with noise (DBSCAN) algorithm or the One-Standard-Deviation (OSD) method?
- Can you explain the differences between univariate and multivariate outlier detection methods, such as the Z-score or Modified Z-score, and when to use each?
- How do methods like the Interquartile Range (IQR) or the Winsorized mean handle extreme values in a dataset?
- What is the relationship between outlier detection and cluster analysis, and how do outliers affect the identification of clusters in a dataset?
- Can you describe the concept of outliers as noise or as indicative of underlying patterns in a dataset, and how this distinction impacts their handling?
- What are some statistical tests, such as the Grubbs test or the Thompson test, that can be used to identify outliers in a dataset, and how do they differ from each other?
- How do outlier detection methods impact the model performance and accuracy when dealing with clustering algorithms, and are there any strategies for addressing outliers in this context?
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