from sklearn.preprocessing import MinMaxScaler()
scaler = MinMaxScaler()
can I directly do:
scaled_data = scaler.fit_transform(mymatrix)
without doing scaler.fit(mymatrix)
first?
If not, why so? What is the difference? Doesnt scaler.fit_transform()
function already compute the variance and mean values too before transforming?
As you can see in the docs here you can, because fit_transform
performs first a fit(),
after that it applies a transform()
.