JuniorTheoryCommon

What does this program print?

Python

import numpy as np
from sklearn.cluster import KMeans

X = np.array([[0], [4], [10], [14]])
model = KMeans(n_clusters=2, n_init=10, random_state=0).fit(X)
print(round(model.inertia_, 1))

16.0

Expecting 8.0, the sum of plain distances: inertia squares each distance to the centroid, so the four distances of 2 give 4 * 2 ** 2 = 16.0.
Expecting 4.0, an average per point: inertia_ is a sum over all points, not a mean, so it grows with the number of points.

What would inertia_ be with n_clusters=4 on the same four points?
Why can inertia not be compared between two datasets of different sizes?

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JuniorTheoryCommon

What does this program print?

Python

import numpy as np
from sklearn.cluster import KMeans

X = np.array([[0], [4], [10], [14]])
model = KMeans(n_clusters=2, n_init=10, random_state=0).fit(X)
print(round(model.inertia_, 1))

16.0

Expecting 8.0, the sum of plain distances: inertia squares each distance to the centroid, so the four distances of 2 give 4 * 2 ** 2 = 16.0.
Expecting 4.0, an average per point: inertia_ is a sum over all points, not a mean, so it grows with the number of points.

What would inertia_ be with n_clusters=4 on the same four points?
Why can inertia not be compared between two datasets of different sizes?

Back to questions Open topic in roadmap →Answer it yourself — Quiz