Can someone explain to me how MinMaxScaler() works? [closed]

Question

Why we are using the MinMaxScaler() and what does it do?

scaler = MinMaxScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
model = LogisticRegression()
model.fit(X_train, y_train)
y_pred = model.predict(X_test)

Answer 1

Essentially, the code is scaling the independent variables so that they lie in the range of 0 and 1. This is important because few variable values might be in thousands and few might be in small ranges. Hence to handle such cases scaling is important. Logistic regression is sensitive to such high values. More about min max is here: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html

Answer 2

The methodology that MinMaxScaler Follow:

X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
X_scaled = X_std * (max - min) + min

Example:

>>> from sklearn.preprocessing import MinMaxScaler
>>> data = [[-1, 2], [-0.5, 6], [0, 10], [1, 18]]
>>> scaler = MinMaxScaler()
>>> print(scaler.fit(data))
MinMaxScaler()
>>> 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. ]]

https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html