What does fit() exactly does here?

Question

Well, basically i want to know what does the fit() function does in general, but especially in the pieces of code down there.

Im taking the Machine Learning A-Z Course because im pretty new to Machine Learning (i just started). I know some basic conceptual terms, but not the technical part.

CODE1:

from sklearn.impute import SimpleImputer

missingvalues = SimpleImputer(missing_values = np.nan, strategy = 'mean', verbose = 0) 

missingvalues = missingvalues.fit(X[:, 1:3])

X[:, 1:3] = missingvalues.transform(X[:, 1:3])

Some other example where I still have the doubt

CODE 2:

from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
print(sc_X)
X_train = sc_X.fit_transform(X_train)
print(X_train)
X_test = sc_X.transform(X_test)

I think that if I know like the general use for this function and what exactly does in general, I'll be good to go. But certaily I'd like to know what is doing on that code

Answer 1

Here is also a nice check-up possibility: https://scikit-learn.org/stable/tutorial/basic/tutorial.html

The fit-method is always to learn something in machine learning.

You normally have the following steps:

1. Seperate your data into two/three datasets
2. Pick one part of your data to learn/train something (normally X_train) with fit
3. Use the learned algorithm you predict something to unseen data (normally X_test) with predict

In your first example: missingvalues.fit(X[:, 1:3]) You are training SimpleImputerbased on your data Xwhere you are only using column 1,2,3, with transform you used this training to overwrite this data.

In your second example: You are training StandardScalerwith X_trainand are using this training for both datasets X_train, X_test, the StandardScaler learnes from X_trainthat means if he learned that 10 has to be converted to 2, he will convert 10 to 2 in both sets X_train, X_test.