Causation versus correlation: when does it matter?
Feature engineering is a critical step in the machine learning pipeline, particularly when dealing with high-dimensional datasets where redundant or irrelevant features can degrade performance. It involves either creating new features from the existing dataset or selecting relevant features from...
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| 格式: | Final Year Project |
| 語言: | English |
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Nanyang Technological University
2025
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/184145 |
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