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scikit-learn Cookbook , Second Edition

You're reading from   scikit-learn Cookbook , Second Edition Over 80 recipes for machine learning in Python with scikit-learn

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Product type Paperback
Published in Nov 2017
Publisher Packt
ISBN-13 9781787286382
Length 374 pages
Edition 2nd Edition
Languages
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Authors (2):
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Trent Hauck Trent Hauck
Author Profile Icon Trent Hauck
Trent Hauck
Julian Avila Julian Avila
Author Profile Icon Julian Avila
Julian Avila
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Toc

Table of Contents (13) Chapters Close

Preface 1. High-Performance Machine Learning – NumPy FREE CHAPTER 2. Pre-Model Workflow and Pre-Processing 3. Dimensionality Reduction 4. Linear Models with scikit-learn 5. Linear Models – Logistic Regression 6. Building Models with Distance Metrics 7. Cross-Validation and Post-Model Workflow 8. Support Vector Machines 9. Tree Algorithms and Ensembles 10. Text and Multiclass Classification with scikit-learn 11. Neural Networks 12. Create a Simple Estimator

Assessing cluster correctness

We talked a little bit about assessing clusters when the ground truth is not known. However, we have not yet talked about assessing k-means when the cluster is known. In a lot of cases, this isn't knowable; however, if there is outside annotation, we will know the ground truth or at least the proxy sometimes.

Getting ready

So, let's assume a world where we have an outside agent supplying us with the ground truth.

We'll create a simple dataset, evaluate the measures of correctness against the ground truth in several ways, and then discuss them:

from sklearn import datasets
from sklearn import cluster

blobs, ground_truth = datasets.make_blobs(1000, centers=3,cluster_std=1.75)
...
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