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Labels

KMeans assigns a cluster label to each example, stored in labels_.

Interpreting the labels

Cluster labels are numbers, not names; rerunning on the same data can swap 0 and 2. That's why clusters are defined by their contents (centre, size, example features), not by their numbers.

for k in range(km.n_clusters):
    uyeler = X[km.labels_ == k]
    print(k, len(uyeler), uyeler.mean(axis=0).round(1))

If the true labels are known (for testing), the comparison must use a criterion independent of the numbers.

Adjusted Rand index

adjusted_rand_score measures how well two labellings agree, independently of the numbers: 1 is full agreement, 0 the random level. It is used to compare clustering methods on synthetic data with a known structure:

from sklearn.metrics import adjusted_rand_score
print(adjusted_rand_score([0, 0, 1, 1], [1, 1, 0, 0]))   # 1.0
print(adjusted_rand_score(y_gercek, km.labels_))

Real projects have no labels; then internal criteria like the silhouette and domain knowledge are used.

Practice Validate the clustering

Train a KMeans(n_clusters=4, n_init=10, random_state=0) on the synthetic data. Print the ARI against the true labels with round(..., 3). Then print the plain accuracy_score(y_gercek, km.labels_) with round(..., 3) and see why the two differ.

Starter code

model.py
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X, y_gercek = make_blobs(n_samples=300, centers=4, cluster_std=0.8, random_state=0)
from sklearn.metrics import adjusted_rand_score, accuracy_score

# buraya yaz

Sample solution

model.py
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X, y_gercek = make_blobs(n_samples=300, centers=4, cluster_std=0.8, random_state=0)
from sklearn.metrics import adjusted_rand_score, accuracy_score

km = KMeans(n_clusters=4, n_init=10, random_state=0).fit(X)
print(round(adjusted_rand_score(y_gercek, km.labels_), 3))
print(round(accuracy_score(y_gercek, km.labels_), 3))

Expected output

0.946
0.497

Questions and answers

Review the lesson questions together with their answers. To test yourself, solve the lesson interactively.

1 If the true labels are [0, 0, 1, 1] and the cluster labels [1, 1, 0, 0], how successful is the clustering?

  • It can't be evaluated
  • Perfect; only the numbers are swapped Correct answer
  • Completely wrong
  • Half right

Explanation: The groups are the same, only the names differ; ARI = 1.

2 Measure the agreement of two labellings.

Question 
ari = ____(y_gercek, km.labels_)
Answer 
ari = adjusted_rand_score(y_gercek, km.labels_)

Explanation: ARI isn't affected by swapped numbers.

3 Match the criteria.

Term Meaning
ARI = 1 Full agreement
ARI ≈ 0 Random level
accuracy_score Misleading for clustering
silhouette Label-free internal criterion

All lessons in Intermediate

1 scikit-learn basics

  1. What is scikit-learn?
  2. Estimator API
  3. The fit method
  4. The predict method
  5. The score method
  6. Summary - scikit-learn

2 Pipeline

  1. What is a Pipeline?
  2. Steps
  3. fit and predict
  4. Advantages
  5. ColumnTransformer
  6. Summary - Pipeline

3 Cross-validation

  1. What is cross-validation?
  2. K-fold
  3. cross_val_score
  4. Stratified
  5. Advantages
  6. Summary - Cross-validation

4 Grid search

  1. What is grid search?
  2. GridSearchCV
  3. param_grid
  4. best_params_
  5. best_score_
  6. Summary - Grid search

5 SVM

  1. What is SVM?
  2. Margin
  3. Kernel
  4. C parameter
  5. Classification
  6. Summary - SVM

6 Project: SVM

  1. Preparing data
  2. Training model
  3. Prediction
  4. Evaluation

7 Random forest

  1. What is random forest?
  2. Ensemble
  3. n_estimators
  4. Bagging
  5. Feature importance
  6. Summary - Random forest

8 Boosting

  1. What is boosting?
  2. AdaBoost
  3. Gradient boosting
  4. Learning rate
  5. Advantages
  6. Summary - Boosting

9 Clustering

  1. What is clustering?
  2. KMeans
  3. Centroid
  4. Elbow method
  5. Labels
  6. Summary - Clustering

10 KMeans in depth

  1. KMeans algorithm
  2. Initialization
  3. n_init
  4. inertia_
  5. random_state
  6. Summary - KMeans

11 Hierarchical clustering

  1. What is hierarchical clustering?
  2. Dendrogram
  3. Agglomerative
  4. Linkage
  5. Advantages
  6. Summary - Hierarchical clustering

12 Project: Clustering

  1. Preparing data
  2. Training KMeans
  3. Label analysis
  4. Printing results

13 PCA

  1. What is PCA?
  2. Variance
  3. n_components
  4. fit_transform
  5. explained_variance_
  6. Summary - PCA

14 Dimensionality reduction

  1. What is dimensionality reduction?
  2. Why reduce?
  3. PCA vs t-SNE
  4. Visualization
  5. Advantages
  6. Summary - Dimensionality reduction

15 Feature selection

  1. What is feature selection?
  2. Variance threshold
  3. SelectKBest
  4. Recursive elimination
  5. Feature importance
  6. Summary - Feature selection

16 Imbalanced data

  1. What is imbalanced data?
  2. SMOTE
  3. Class weight
  4. Oversampling
  5. Metric selection
  6. Summary - Imbalanced data

17 Project: Features

  1. Feature extraction
  2. Feature selection
  3. Training model
  4. Evaluation

18 Ensemble methods

  1. What is ensemble?
  2. Voting
  3. Bagging
  4. Boosting
  5. Stacking
  6. Summary - Ensemble

19 Regularization

  1. What is regularization?
  2. L1 (Lasso)
  3. L2 (Ridge)
  4. alpha
  5. ElasticNet
  6. Summary - Regularization

20 Intermediate final

  1. Challenge 1
  2. Challenge 2
  3. Challenge 3
  4. Challenge 4