K-means via scipy vs scikit-learn - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the KMeans class from scikit-learn.

SciPy

from sklearn.cluster import [1]

Drag options to blanks, or click blank then click option'

AKmean

BKMeans

Ckmeans

DCluster

Attempts:

3 left

💡 Hint

Common Mistakes

Using lowercase 'kmeans' which is not recognized.

Misspelling the class name.

The correct class name to import for K-means clustering in scikit-learn is KMeans with capital K and M.

2fill in blank

medium

Complete the code to run K-means clustering with 3 clusters using scikit-learn.

SciPy

model = KMeans(n_clusters=[1])
model.fit(data)

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A3

B0

C1

D5

Attempts:

3 left

💡 Hint

Common Mistakes

Setting n_clusters to 0 or 1 which is invalid or trivial.

Using a number different from the intended cluster count.

To cluster data into 3 groups, set n_clusters=3.

3fill in blank

hard

Fix the error in the code to compute K-means using scipy's kmeans function.

SciPy

from scipy.cluster.vq import kmeans
centroids, distortion = kmeans(data, [1])

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A[3]

B'3'

C(3,)

D3

Attempts:

3 left

💡 Hint

Common Mistakes

Passing the number as a string like '3'.

Passing the number inside a list or tuple.

The kmeans function expects the number of clusters as an integer, not a string or list.

4fill in blank

hard

Fill both blanks to create a dictionary of cluster labels and their counts using scikit-learn.

SciPy

labels = model.[1]
counts = {label: list(labels).[2](label) for label in set(labels)}

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Alabels_

Bcount

Dlabel_

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'labels' instead of 'labels_' attribute.

Using 'count' as a variable instead of a method.

In scikit-learn, cluster labels are accessed with labels_. To count occurrences, use the list method count.

5fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each word to its length if length is greater than 3.

SciPy

result = {word:: len(word) for word in words if len(word) [1] 3 and word [2] 'data'}

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A:

B>

C!=

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dictionary comprehension.

Using '==' instead of '!=' to exclude 'data'.

The dictionary comprehension syntax uses ':' between key and value. We check if length is greater than 3 and word is not equal to 'data'.