What is min-max normalization and how is it used?
An example of an AI chatbot conversation about min-max normalization in data preprocessing.
What is min-max normalization and how does it work?
Min-max normalization is a technique to scale data to a fixed range, usually [0, 1]. It transforms features by subtracting the minimum value and dividing by the range of the feature.
Can you give me an example of how to apply it?
Sure! If you have a dataset with values ranging from 10 to 50, to normalize a value of 30, you would use the formula: (30 - 10) / (50 - 10) = 0.5.
What are the advantages of using min-max normalization?
It preserves the relationships between the data points and is useful for algorithms that rely on distance calculations, like k-NN.
Are there any drawbacks to this method?
Yes, it can be sensitive to outliers, as they can significantly affect the minimum and maximum values used for scaling.
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