Generalized Omni-dimensional Development

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ai_data_detection [2025/05/25 15:06] – [Key Features] eagleeyenebulaai_data_detection [2025/05/25 15:09] (current) – [Best Practices] eagleeyenebula
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 ===== Best Practices ===== ===== Best Practices =====
 1. **Use Incremental Checks:** Perform quality checks at different stages of the pipeline (e.g., after loading raw data and after preprocessing steps). 1. **Use Incremental Checks:** Perform quality checks at different stages of the pipeline (e.g., after loading raw data and after preprocessing steps).
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 2. **Automate Logging:** Set up centralized logging for tracking data issues across multiple datasets. 2. **Automate Logging:** Set up centralized logging for tracking data issues across multiple datasets.
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 3. **Adapt Custom Methods:** Extend the module for domain-specific checks, such as outlier detection, range checks, or invalid category detection. 3. **Adapt Custom Methods:** Extend the module for domain-specific checks, such as outlier detection, range checks, or invalid category detection.
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 4. **Handle Issues Early:** Address identified data issues before training machine learning models. 4. **Handle Issues Early:** Address identified data issues before training machine learning models.
  
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 **Example: Adding Invalid Category Detection** **Example: Adding Invalid Category Detection**
-```python+<code> 
 +python
 def has_invalid_categories(data, valid_categories): def has_invalid_categories(data, valid_categories):
     for col in data.select_dtypes(include=['object']):     for col in data.select_dtypes(include=['object']):
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             return True             return True
     return False     return False
-```+</code>
  
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ai_data_detection.1748185583.txt.gz · Last modified: 2025/05/25 15:06 by eagleeyenebula