scikit learn: pipeline

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.fit(X, y).predict(X)
plt.scatter(pred, y)

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