What is the purpose of outlier detection in data cleansing?
- To fill missing values in the dataset
- To identify and remove data points that deviate significantly from the rest of the dataset
- To merge duplicate records in the dataset
- To standardize the format of the dataset
Outlier detection in data cleansing aims to identify and remove data points that deviate significantly from the rest of the dataset. Outliers can skew statistical analyses and machine learning models, leading to inaccurate results or biased predictions. Detecting and addressing outliers helps improve the quality and reliability of the dataset for downstream analysis and modeling tasks.
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