@KirkDBorne: Outlier Detection in Python — http://amzn.to/49GMXMh from @ManningBooks —————— #Statistics #DataScientist #Analytics #D…
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Promotion for the book 'Outlier Detection in Python' from Manning Publications, covering various outlier detection techniques with an emphasis on explainability.
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Outlier Detection in Python — https://t.co/gPuAijwpPe from @ManningBooks —————— #Statistics #DataScientist #Analytics #DataScience https://t.co/kWoxcVE8p7
Outlier Detection in Python: 9781633436473: Computer Science Books @ Amazon.com
Source: https://www.amazon.com/Outlier-Detection-Python-Brett-Kennedy/dp/1633436470?&linkCode=sl1&tag=kirkdborne-20&linkId=4315057001e458504965e5cbc563c5c8&language=en_US&ref_=as_li_ss_tl Many data analysts think of outliers as extreme numerical values or rare categorical values, but they can also be rare combinations of columnar values within the same record. These outlier scenarios are learned with experience. Basic outlier detection strategies such as box plots, calculating IQR, or implementing PCA are not enough.
Outlier Detection in Python offers a thorough exploration of various outlier types and detectors, many optimized for specific data needs. If you work with large or complex datasets, this book is essential. It will elevate your outlier mitigation skills, improving data analysis and modeling quality.
The book emphasizes explainability, crucial for understanding why certain items were flagged as outliers, such as in security threats or fraud detection. This focus on interpretability and explainable AI (XAI) is invaluable for any data analyst.
Whether you’re experienced or just starting, this book provides a solid foundation for mastering outlier detection.
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