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Book Overview & Buying
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Table Of Contents
scikit-learn Cookbook - Third Edition
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When working with datasets, features can have vastly different scales. For instance, a feature representing age may range from 0 to 100, while another feature representing income could range from 0 to 100,000. Many ML algorithms, such as KNN and gradient descent-based methods (e.g., linear regression), are sensitive to these differences in scale. Therefore, scaling helps ensure that no single feature dominates the learning process. This recipe covers the three most commonly used scaling techniques in ML.
The following are key concepts. It is worth noting that sometimes these two terms are used interchangeably, but they are not the same and should not be implemented as such!
We will use the previously defined iterative_imputed_df DataFrame...