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sklearn.preprocessing._data.MinMaxScaler

sklearn.preprocessing._data.MinMaxScaler

Visibility: public Uploaded 02-07-2023 by Sonia sklearn==1.1.3 numpy>=1.17.3 scipy>=1.3.2 joblib>=1.0.0 threadpoolctl>=2.0.0 0 runs
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  • openml-python python scikit-learn sklearn sklearn_1.1.3
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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. This transformation is often used as an alternative to zero mean, unit variance scaling.

Parameters

clipSet to True to clip transformed values of held-out data to provided `feature range` .. versionadded:: 0.24default: false
copySet to False to perform inplace row normalization and avoid a copy (if the input is already a numpy array)default: true
feature_rangeDesired range of transformed datadefault: [0, 1]

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