[Solved] Find the bug in this scikit-learn KMeans usage:from sklearn.cluster import KMeans import numpy as np data = np.array([[1, 2], [3, 4], [5, 6]]) kmeans = KMeans(n_clusters=3) kmeans.... | SciPy

SciPy - Clustering and Distance

Find the bug in this scikit-learn KMeans usage:

from sklearn.cluster import KMeans
import numpy as np

data = np.array([[1, 2], [3, 4], [5, 6]])
kmeans = KMeans(n_clusters=3)
kmeans.fit_predict(data)
print(kmeans.labels_)

Alabels_ attribute is not available after fit_predict.

Bn_clusters must be less than number of data points.

Cfit_predict returns labels but is not assigned to a variable.

DData must be a list, not a numpy array.

Step-by-Step Solution

Solution:

Step 1: Understand fit_predict behavior
fit_predict returns cluster labels but here return value is ignored.
Step 2: Check labels_ attribute usage
labels_ is set after fit or fit_predict, so print(kmeans.labels_) works correctly.
Step 3: Validate n_clusters parameter
n_clusters=3 but data has only 3 points; scikit-learn allows n_clusters equal to number of points, but not greater. Here, it's equal, so no error.
Final Answer:
fit_predict returns labels but is not assigned to a variable. -> Option C
Quick Check:
Use fit_predict output properly = fit_predict returns labels but is not assigned to a variable. [OK]

Quick Trick: Assign fit_predict output to variable if needed [OK]

Common Mistakes:

Ignoring fit_predict return value
Misunderstanding labels_ availability
Confusing data type requirements

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Find the bug in this scikit-learn KMeans usage:

Step 1: Understand fit_predict behavior

Step 2: Check labels_ attribute usage

Step 3: Validate n_clusters parameter

Final Answer:

Quick Check:

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