Machine Learning for OpenCV 4 - Second Edition

Scaling features to a range

An alternative to scaling features to zero mean and unit variance is to get features to lie between a given minimum and maximum value. Often, these values are zero and one, so that the maximum absolute value of each feature is scaled to unit size. In scikit-learn, this can be achieved using MinMaxScaler:

In [7]: min_max_scaler = preprocessing.MinMaxScaler()...     X_min_max = min_max_scaler.fit_transform(X)...     X_min_maxOut[7]: array([[ 0.33333333, 0. , 1. ],               [ 1. , 0.66666667, 0.33333333],               [ 0. , 1. , 0. ]])

By default, the data will be scaled to fall within 0 and 1. We can specify different ranges by passing a keyword argument, feature_range, to the MinMaxScaler constructor:

In [8]: min_max_scaler = preprocessing.MinMaxScaler(feature_range ...