o write some code: Cluster MNIST with k-means (you know optimal k already). Assign a label to each cluster by the most popular y (classes from 0 to 9) in that cluster.
o write some code: Cluster MNIST with k-means (you know optimal k already). Assign a label to each cluster by the most popular y (classes from 0 to 9) in that cluster.
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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to write some code:
- Cluster MNIST with k-means (you know optimal k already).
- Assign a label to each cluster by the most popular y (classes from 0 to 9) in that cluster.
Now we can compute accuracy_score of our cluster labels comparing them with true label of each point.
This way we can get an idea of how good our clustering is without looking at it (because you can't really look at 64 dimensional points, right?).
q1: What accuracy_score did you get?
Hint: you can use np.bincount(x):
Count number of occurrences of each value in array of non-negative ints. Each bin gives the number of occurrences of its index value in `x`.Hint: use KMeans(..., random_state=0) for reproducible results.
from sklearn.metrics import accuracy_score
# YOUR CAN WRITE CODE HERE
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