SCALING
Task that makes data more omogeneous. It’s used when there is a massive presence of outliers, which could affect too much the model during the training step.
Scalers allow to scale values in such a way that these values have a more omogeneous distribution. The most famous one is the MinMaxScaler:
`from sklearn.preprocessing import MinMaxScaler
It transforms values to make them stay in a [0,1] range.
USAGE:
#1) Initiate the scaler
scaler = MinMaxScaler()
#2)Reshaping data --> we want them in a column/row-array-like form (1-Dim)
#2.1) Column
X.reshape(-1,1)
#2.2) Row
X.reshape(1,-1)
#2)fitting and transforming the data
Xs = scaler.fit_transform(X)
Now we have a scaled dataset!