Machine Learning News Hubb
Advertisement Banner
  • Home
  • Machine Learning
  • Artificial Intelligence
  • Big Data
  • Deep Learning
  • Edge AI
  • Neural Network
  • Contact Us
  • Home
  • Machine Learning
  • Artificial Intelligence
  • Big Data
  • Deep Learning
  • Edge AI
  • Neural Network
  • Contact Us
Machine Learning News Hubb
No Result
View All Result
Home Machine Learning

Unsupervised Learning: Building Score Metrics for Cluster Points | by Samson Afolabi | Sep, 2022

admin by admin
September 8, 2022
in Machine Learning


Measuring differences within similar cluster points

A cluster of small brown mushroom. Photo by Nareeta Martin on Unsplash

Clustering is an unsupervised machine learning technique used for discovering interesting patterns in data. An example would be grouping similar customers based on their behavior, building a spam filter, identifying fraudulent or criminal activity.

In Clustering, similar items(or data points) are grouped together. However, we do not only want to group similar items together, we would also like to measure how similar or different they are. To solve this, we can easily create a scoring algorithm.

In this example, I use a simple k-means clustering method. You can read about it here. We generate isotropic Gaussian blobs for clustering with sklearn.datasets.make_blobs.

Next we build a simple k-means algorithm with 3 clusters and get the centroids of these clusters.

Now, to score each of the points in the different clusters, we could estimate how close they are to the center of the cluster and compare that to the farthest point in the cluster. In this example, our dataset involves 2 columns, so we could easily measure the sum of their squared differences. These distances can be converted to percentages for easy interpretation.

The measurements would not only give us an estimate of how far a point is to the center of a cluster, but how close they are to possibly falling off to the next cluster. This is particularly interesting for problems like customer segmentation, in which case we would like to test how each marketing approach taken, affects the customer.

I hope this was helpful for you. Looking forward to your comments here, meanwhile you can also follow me on twitter and Linkedin.

You can also check out my article on “Building Customer Clusters Using Unsupervised Machine Learning” here.

Vielen Dank😊





Source link

Previous Post

Introduction to ML in Production. Digging into the machine learning… | by Javier Fernandez | Sep, 2022

Next Post

Some of The Most Important SQL Commands

Next Post

Some of The Most Important SQL Commands

Hierarchical Clustering of Images with Python | by Dahi Nemutlu | Sep, 2022

Bayesian A/B Testing in R. Analyze social media performance with… | by Hannah Roos | Sep, 2022

Related Post

Artificial Intelligence

Dates and Subqueries in SQL. Working with dates in SQL | by Michael Grogan | Jan, 2023

by admin
January 27, 2023
Machine Learning

ChatGPT Is Here To Stay For A Long Time | by Jack Martin | Jan, 2023

by admin
January 27, 2023
Machine Learning

5 steps to organize digital files effectively

by admin
January 27, 2023
Artificial Intelligence

Explain text classification model predictions using Amazon SageMaker Clarify

by admin
January 27, 2023
Artificial Intelligence

Human Resource Management Challenges and The Role of Artificial Intelligence in 2023 | by Ghulam Mustafa Shoaib | Jan, 2023

by admin
January 27, 2023
Deep Learning

Training Neural Nets: a Hacker’s Perspective

by admin
January 27, 2023

© 2023 Machine Learning News Hubb All rights reserved.

Use of these names, logos, and brands does not imply endorsement unless specified. By using this site, you agree to the Privacy Policy and Terms & Conditions.

Navigate Site

  • Home
  • Machine Learning
  • Artificial Intelligence
  • Big Data
  • Deep Learning
  • Edge AI
  • Neural Network
  • Contact Us

Newsletter Sign Up.

No Result
View All Result
  • Home
  • Machine Learning
  • Artificial Intelligence
  • Big Data
  • Deep Learning
  • Edge AI
  • Neural Network
  • Contact Us

© 2023 JNews - Premium WordPress news & magazine theme by Jegtheme.