I have seen pages where they mention 5 methods of building models.
1) All-in 2) Backward Elimination 3) Forward Selection 4) Bidirectional Elimination 5) Score Comparision
I usually implement a linear regression or any algorithm using
from sklearn.linear_model import LinearRegression lr = LinearRegression() lr.fit(X, y) y_pred = lr.predict(X_test)
How can one implement those 5 methods of building models?
Can anyone explain the importance of this and What is most commonly used one out of these?