Scaling techniques in machine learning are crucial methods used to adjust the range of values that independent variables, or features, can take. The main goal of these techniques is to ensure that different features, which may have varying units or magnitudes, are on a comparable scale. This is important because many machine learning algorithms are sensitive to the relative sizes of numerical values, which can lead to biased results if not addressed properly.
The Power of Scaling Techniques for Optimal Model Performance
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