January 2025
Intermediate to advanced
436 pages
11h 10m
English
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8.4 What Could Go Wrong?
Automating feature engineering and model selection can significantly simplify the machine learning process, but there are some potential pitfalls to consider. Understanding these can help you avoid common mistakes and ensure that automated tools are used effectively.
8.4.1 Over-Reliance on Automated Pipelines
- AutoML tools make it easy to build models, but they can lead to over-reliance on automated processes, which might not consider specific nuances in the data.
- Solution: Treat AutoML results as a baseline and consider further fine-tuning or manual adjustments based on domain knowledge.
8.4.2 Data Leakage
- Automated feature engineering may inadvertently introduce data leakage, particularly if certain features or data transformations ...
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