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sklearn.preprocessing._data.MinMaxScaler

sklearn.preprocessing._data.MinMaxScaler

Visibility: public Uploaded 18-05-2020 by Marc Zöller sklearn==0.22.1 numpy>=1.6.1 scipy>=0.9 0 runs
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  • openml-python python scikit-learn sklearn sklearn_0.22.1
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Transform features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one. The transformation is given by:: X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0)) X_scaled = X_std * (max - min) + min where min, max = feature_range. The transformation is calculated as:: X_scaled = scale * X + min - X.min(axis=0) * scale where scale = (max - min) / (X.max(axis=0) - X.min(axis=0)) This transformation is often used as an alternative to zero mean, unit variance scaling.

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