logo

... scikit learn: pipeline



Notes

Let's add a preprocessing step by introducing it in a pipeline.

from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import load_boston
from sklearn.pipeline import Pipeline
import matplotlib.pylab as plt

X, y = load_boston(return_X_y=True)

pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", KNeighborsRegressor())
])
pred = pipe.predict(X)
plt.scatter(pred, y)

Note that this pipe also has the same .fit(X, y).predict(X) api.

Feedback? See an issue? Something unclear? Feel free to mention it here.



If you want to be kept up to date, consider signing up for the newsletter.