What is the difference between Z-score and Min-Max normalization?
An example of an AI chatbot conversation about normalization techniques in data processing.
Can you explain the difference between Z-score normalization and Min-Max normalization?
Sure! Z-score normalization standardizes data based on the mean and standard deviation, while Min-Max normalization scales data to a specific range, usually 0 to 1.
When should I use Z-score normalization instead of Min-Max normalization?
Use Z-score normalization when your data follows a normal distribution and you want to identify outliers. Min-Max is better for bounded data.
Are there any drawbacks to using either method?
Yes, Z-score can be sensitive to outliers, while Min-Max can distort data if there are outliers since it compresses all values into a range.
Can you give me an example of when to use each?
Certainly! Use Z-score for datasets like test scores that are normally distributed, and Min-Max for image pixel values that need to be scaled between 0 and 1.
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