8.12 ML Methods

k = 2, Sum of squared distances (inertia): 560.625
Cluster 1:
['naive Bayes' 'kNN' 'Linear Classifier' 'Linear Regression'
 'Logistic Regression' 'SVM' 'Kmeans' 'GMM']
Cluster 2:
['Trees' 'Rules' 'Associations']
k = 3, Sum of squared distances (inertia): 293.5
Cluster 1:
['naive Bayes' 'GMM']
Cluster 2:
['Trees' 'Rules' 'Associations']
Cluster 3:
['kNN' 'Linear Classifier' 'Linear Regression' 'Logistic Regression' 'SVM'
 'Kmeans']
k = 4, Sum of squared distances (inertia): 207.5
Cluster 1:
['kNN' 'Linear Classifier' 'Linear Regression' 'Logistic Regression' 'SVM'
 'Kmeans']
Cluster 2:
['naive Bayes']
Cluster 3:
['Trees' 'Rules' 'Associations']
Cluster 4:
['GMM']
k = 5, Sum of squared distances (inertia): 147.99999999999997
Cluster 1:
['kNN' 'Linear Regression' 'Logistic Regression' 'SVM' 'Kmeans']
Cluster 2:
['naive Bayes']
Cluster 3:
['Trees' 'Rules' 'Associations']
Cluster 4:
['GMM']
Cluster 5:
['Linear Classifier']