What are some common data validation errors in AI?
Common data validation errors in AI include missing values, incorrect data types, and outliers.
Can you explain how missing values affect AI models?
Missing values can lead to biased results or reduced model accuracy, as the model may not learn from incomplete data.
What are some techniques to handle missing values?
Techniques include imputation, where you fill in missing values, or removing records with missing data.
How do outliers impact AI training?
Outliers can skew the results and lead to overfitting, making the model less generalizable.
What tools can help with data validation?
Tools like Pandas, OpenRefine, and data validation libraries in Python can assist in identifying and correcting errors.
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