CodeBlanks Logo Icon CodeBlanks
Login Register

n_init

n_init sets the number of different initializations for KMeans.

n_init

n_init is how many times KMeans runs with different starts; the solution with the lowest inertia is kept. It is the simplest and most effective insurance against local optima:

km = KMeans(n_clusters=5, n_init=10, random_state=0).fit(X)
# 10 separate starts, the best is kept

In newer scikit-learn versions the default is n_init="auto": 1 run with k-means++, 10 runs with random initialisation.

Time and quality

As n_init grows the time rises linearly while the gain diminishes. On large datasets MiniBatchKMeans runs much faster by updating with a small part of the data in each round:

from sklearn.cluster import MiniBatchKMeans
mb = MiniBatchKMeans(n_clusters=5, batch_size=256, n_init=3, random_state=0).fit(X)
print(round(mb.inertia_))   # close to KMeans, much faster

With millions of examples MiniBatchKMeans may be the only practical option.

Practice The effect of n_init

Train KMeans (5 clusters, random_state=2) with init="random" for n_init = 1, 5, 20. On each line print n_init and the inertia as an integer with round.

Starter code

model.py
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X, _ = make_blobs(n_samples=500, centers=5, cluster_std=1.0, random_state=3)

# buraya yaz

Sample solution

model.py
import numpy as np
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

X, _ = make_blobs(n_samples=500, centers=5, cluster_std=1.0, random_state=3)

for n in [1, 5, 20]:
    km = KMeans(n_clusters=5, init="random", n_init=n, random_state=2).fit(X)
    print(n, round(km.inertia_))

Expected output

1 2221
5 1000
20 1000

Questions and answers

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

1 With <code>n_init=10</code>, which solution is returned?

  • The one with the lowest inertia Correct answer
  • The last run
  • Their average
  • The first run

Explanation: Each run is independent; the best is chosen.

2 Run the algorithm with 20 different starts.

Question 
km = KMeans(n_clusters=5, ____=20, random_state=0)
Answer 
km = KMeans(n_clusters=5, n_init=20, random_state=0)

Explanation: n_init is the number of starts; max_iter is the round limit within one run.

3 Match the parameters.

Term Meaning
n_init Number of starts
max_iter Round limit
MiniBatchKMeans Fast mini-batch version
batch_size Batch size

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