I have learned the concept of pipeline in Scikit-learn. But I don’t know exactly how I apply this to my model. I tried to do it, but I found an error that I’m unable to get it. Can you explain me the error, what it is trying to say?
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=42)
from sklearn.linear_model import LinearRegression
pipeline = make_pipeline(LinearRegression())
pipeline.fit(X_train, y_train)
NameError: name 'make_pipeline' is not defined
I want to understand the different steps of a pipeline that are crucial for successfully building and deploying machine learning models. Can someone help me with the code example that shows how the pipeline works?