What are some common feature engineering mistakes to avoid?
An example of an AI chatbot conversation about common feature engineering mistakes.
What are some common feature engineering mistakes to avoid?
Some common mistakes include ignoring domain knowledge, not handling missing values properly, and over-engineering features.
Can you explain why ignoring domain knowledge is a mistake?
Ignoring domain knowledge can lead to irrelevant features that don't capture the underlying patterns in the data.
What about handling missing values? Why is that important?
Properly handling missing values is crucial because they can skew your model's performance and lead to inaccurate predictions.
How can I avoid over-engineering features?
To avoid over-engineering, focus on simplicity and only create features that add significant value to your model.
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