Min-Max Scaler in SKlearn - Python

Introduction

The Min-Max Scaler is a data normalization technique that scales features to a fixed range (usually [0,1]). It works by subtracting the minimum value of the feature, and then scaling the feature by the range of the maximum and minimum values. This technique helps in improving the performance of machine learning models and ensures that all features are on an equal footing.

The Min-Max Scaler is implemented in Python's Scikit-Learn library through the MinMaxScaler class.

Example

Here is an example of how to use the Min-Max Scaler in Python:

from sklearn.preprocessing import MinMaxScaler
import numpy as np

data = np.array([[1, 2], [2, 4], [3, 6], [4, 8]])
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data)

print(scaled_data)

This code will output the following normalized data:

[[0.   0.   ]
 [0.333 0.333]
 [0.667 0.667]
 [1.   1.   ]]

Conclusion

The Min-Max Scaler is a useful data normalization technique that helps in improving the performance of machine learning models. It is implemented in Python's Scikit-Learn library through the MinMaxScaler class.