added changes in other notebooks

Author: SebastienMelo

jupyter-book/clustering/clustering_quiz_m4_01.md

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+# ✅ 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/evaluation_quiz_kmeans_not_scaled.svg)
+![K-means on scaled features](../../figures/evaluation_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_
+```

jupyter-book/clustering/clustering_quiz_m4_02.md

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+# ✅ Quiz M4.02
+
+```{admonition} Question
+If we increase `min_cluster_size` in HDBSCAN, what happens to the number of
+points labeled as noise?
+
+- a) It decreases.
+- b) It increases.
+- c) It stays the same.
+- d) HDBSCAN fails to converge.
+
+_Select a single answer_
+
+```
+
++++
+
+```{admonition} Question
+What happens to k-means centroids in the presence of outliers?
+
+- a) They move towards the outliers assigned to their cluster.
+- b) They are not sensitive to outliers.
+- c) If a centroid is initialized on an outlier, it may remain isolated in
+  subsequent iterations.
+
+_Select all answers that apply_
+
+```
+
++++
+
+```{admonition} Question
+A `KMeans` instance with `n_clusters=10` is used to transform the latitude and
+longitude in a supervised learning pipeline. Provided the original dataset consists of
+`n_features`, including those two, how many features are passed to
+the final estimator of the pipeline?
+
+- a) `n_features` + 10
+- b) `n_features` + 8
+- c) `n_features` - 2
+- d) `n_features`
+
+_Select a single answer_
+
+```