The MinMaxScaler is a data preprocessing technique in the Python scikit-learn library (sklearn.preprocessing) that is used for scaling numerical features to a specified range, usually between 0 and 1. It rescales each feature individually by subtracting the minimum value of the feature and then dividing by the range (maximum value minus minimum value). This preprocessing step is often performed to make the features more suitable for certain machine learning algorithms or to ensure consistency in the scale of different features. The MinMaxScaler is particularly useful when the feature values have different units of measurement or ranges.
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