scikit-learn MinMaxScaler produces slightly different results than a NumPy implemantation

Question

I compared the scikit-learn Min-Max scaler from its `preprocessing` module with a "manual" approach using NumPy. However, I noticed that the result is slightly different. Does anyone have a explanation for this? Using the following equation for Min-Max scaling: which should be the same as the scikit-learn one: `(X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))` I am using both approaches as follows: ``` def numpy_minmax(X): xmin = X.min() return (X - xmin) / (X.max() - xmin) def sci_minmax(X): minmax_scale = preprocessing.MinMaxScaler(feature_range=(0, 1), copy=True) return minmax_scale.fit_transform(X) ``` On a random sample: ``` import numpy as np np.random.seed(123) # A random 2D-array ranging from 0-100 X = np.random.rand(100,2) X.dtype = np.float64 X *= 100 ``` The results are slightly different: ``` from matplotlib import pyplot as plt sci_mm = sci_minmax(X) numpy_mm = numpy_minmax(X) plt.scatter(numpy_mm[:,0], numpy_mm[:,1], color='g', label='NumPy bottom-up', alpha=0.5, ma…

Accepted Answer

`scikit-learn` processes each feature individually. So, you need to specify `axis=0` when taking `min`, otherwise `numpy.min` would be the min on all the elements of the array, not each column separately:

```
>>> xs
array([[1, 2],
       [3, 4]])
>>> xs.min()
1
>>> xs.min(axis=0)
array([1, 2])
```

same thing for `numpy.max`; so the correct function would be:

```
def numpy_minmax(X):
    xmin =  X.min(axis=0)
    return (X - xmin) / (X.max(axis=0) - xmin)
```

Doing so you will get an exact match:

scikit-learn MinMaxScaler produces slightly different results than a NumPy implemantation

Solution

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