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... scikit learn: settings



Notes

Feel free to play around with the code below.

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(n_neighbors=1))
])
pred = pipe.predict(X)
plt.scatter(pred, y)

Note the effect of setting n_neighbors. What does the plot tell us? Is it giving us a trustworthy summary?

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



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