CODEWITHSUNDEEP
pythonscikit-learn
Angus Luo
Angus Luo

Reputation: 65

MinMaxScaler in Scikit Learn seems to be returning scaled values not belonging to the range of values specified by the `feature_range` parameter

This is the example from official: http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html

>>> from sklearn.preprocessing import MinMaxScaler
>>>
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler(copy=True, feature_range=(0, 1))
>>> print(scaler.data_max_)
[  1.  18.]
>>> print(scaler.transform(data))
[[ 0.    0.  ]
 [ 0.25  0.25]
 [ 0.5   0.5 ]
 [ 1.    1.  ]]
>>> print(scaler.transform([[2, 2]]))
[[ 1.5  0. ]]

The official document is as below.

transform(X)[source] Scaling features of X according to feature_range.

Parameters: X : array-like, shape [n_samples, n_features]

Input data that will be transformed.

Why am I seeing [[ 1.5 0. ]] as an output when the default value for feature_range parameter is (0, 1)?

Upvotes: 3

Views: 1405

Answers (1)

Afshin Amiri
Afshin Amiri

Reputation: 3583

Consider the transform operation like this:

import numpy as np
data = np.array(data)
f1 = data[:,0]
mn = f1.min()
mx = f1.max()
(f1- mn)/ (mx - mn)

note: the transform operation is constructed from your initial data. once the tranform operation is constructed, it will operate on your secondary data and does not re-scale it again. you may have problem with parameter feature_range. In that case you should know that after minmaxtransform it rescale the data.

Upvotes: 2

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