min max scaler sklearn

from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaler.fit_transform(X_train)
scaler.transform(X_test)

Here is what the above code is Doing:
1. Creating a MinMaxScaler object
2. Fitting the scaler to the training data
3. Transforming both the training and test data

The scaler works by default on a range of 0 to 1. This can be changed by specifying a feature_range parameter.

The scaler is only fit to the training data. This means that it’s internal parameters will be based on the training data.

The scaler is only used to transform the data. This means that the test data will be transformed using the internal parameters that were learned from the training data.

This is important because you should never use the test data to make any kind of decision about the model.

The scaler is fit and transform on the training data. This means that the training data will be scaled based on the training data and the test data will be scaled based on the training data.

This is important because you want your model to be trained on the actual data that it will see in the real world.

The scaler is only used to transform the data. This means that the training data will be transformed using the internal parameters that were learned from the training data.

This is important because you want your model to be trained on the actual data that it will see in the real world.

The scaler is fit and transform on the training data. This means that the training data will be scaled based on the training data and the test data will be scaled based on the training data.

This is important because you want your model to be trained on the actual data that it will see in the real world.

The scaler is only used to transform the data. This means that the training data will be transformed using the internal parameters that were learned from the training data.

This is important because you want your model to be trained on the actual data that it will see in the real world.

The scaler is fit and transform on the training data. This means that the training data will be scaled based on the training data and the test data will be scaled based on the training data.

This is important because you want your model to be trained on the actual data that it will see in the real world.

The scaler is only used to transform the data. This means that the training data will be transformed using the internal parameters that were learned from the training data.