Pipelines allow you to easily connect data processing together in Sklearn. For example, you could create a pipeline to run scaling then train a model. Then, whenever you call your pipeline, you don't have to remember to scale the data first. In this article, we will learn how to use pipelines in Sklearn.
To build a pipeline, we pass a list of tuples (key, the processor) to the
Pipeline class. We can then use the
fit method on our data similar to how we do with other models.
from sklearn.svm import SVC from sklearn.preprocessing import StandardScaler from sklearn import make_classification from sklearn.model_selection import train_test_split from sklearn.pipeline import Pipeline X, y = make_classification(random_state=0) X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0) pipe = Pipeline([ ('scaler', StandardScaler()), ('svc', SVC()) ]) pipe.fit(X_train, y_train) print(pipe.score(X_test, y_test))