# ✅ Quiz M4.01

```{admonition} Question
Imagine you work for a music streaming platform that hosts a vast library of
songs, playlists, and podcasts. You have access to detailed listening data from
millions of users. For each user, you know their most-listened genres, the
devices they use, their average session length, and how often they explore new
content.

You want to segment users based on their listening patterns to improve
personalized recommendations, without relying on rigid, predefined labels like
"pop fan" or "casual listener" which may fail to capture the complexity of
their behavior.

What kind of problem are you dealing with?

- a) a supervised task
- b) an unsupervised task
- c) a classification task
- d) a clustering task

_Select all answers that apply_
```

+++

```{admonition} Question
The plots below show the cluster labels as found by k-means with 3 clusters, only
differing in the scaling step. Based on this, which conclusions can be obtained?

![K-means on original features](../../figures/clustering_quiz_kmeans_not_scaled.svg)
![K-means on scaled features](../../figures/clustering_quiz_kmeans_scaled.svg)

- a) without scaling, cluster assignment is dominated by the feature in the vertical axis
- b) without scaling, cluster assignment is dominated by the feature in the horizontal axis
- c) without scaling, both features contribute equally to cluster assignment

_Select a single answer_
```

+++

```{admonition} Question
Which of the following statements correctly describe factors that affect the
stability of k-means clustering across different resampling iterations of the data?

- a) K-means can produce different results on resampled datasets due to
  sensitivity to initialization.
- b) If data is unevenly distributed, the stability improves when increasing the
  parameter `n_init` in the "k-means++" initialization.
- c) Stability under resampling is guaranteed after feature scaling.
- d) Increasing the number of clusters always reduces the variability of
  results across resamples.

_Select all answers that apply_
```

+++

```{admonition} Question
Which of the following statements correctly describe how WCSS (within-cluster
sum of squares, or inertia) behaves in k-means clustering?

- a) For a fixed number of clusters, WCSS is lower when clusters are compact.
- b) For a fixed number of clusters, WCSS is lower for wider clusters.
- c) For a fixed number of clusters, lower WCSS implies lower computational cost
  during training.
- d) Assuming `n_init` is large enough to ensure convergence, WCSS always
  decreases as the number of clusters increases.

_Select all answers that apply_
```

+++

```{admonition} Question
Which of the following statements correctly describe differences between
supervised and unsupervised clustering metrics?

- a) Supervised clustering metrics such as ARI and AMI require access to ground
  truth labels to evaluate clustering performance.
- b) WCSS and the silhouette score evaluate internal cluster structure without
  needing reference labels.
- c) V-measure is zero when labels are assigned completely at random.
- d) Supervised clustering metrics are not useful if the number of clusters does
  not match the number of predefined classes.

_Select all answers that apply_
```