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Home Page: https://rubixml.com
License: MIT License
Use the K Nearest Neighbors algorithm to predict the probability of a divorce with high accuracy.
Home Page: https://rubixml.com
License: MIT License
What do they numbers in the dataset stand for ? It's 0 to 4. Is that the 'likeliness' from Negative to most Positive ? It's weird that they are from 0 to 4. Never seen anything similar i mean.
I have experimented quite a bit with this example over the past few days as an initial approach towards learning Rubix, and I have noticed that the confidence with which the KNN formulates its predictions is always 1 for the up class, and never in-between.
I would expect situations where the dominant class is not so clear, as the K neighbors may very well be mixed between the two.
I have also looked at the source code for the probaSample function and it looks correct to me. Have you experienced this result in your testing with the Divorce predictor? Could it be because of the nature of the problem, i.e., could the 54-dimensional feature vectors reside within well-defined boundaries for each of the two classes, married and divorced?
We're looking for someone from the community to lay down some prose and help out your fellow developers. You'll have the chance to dive deep into the K Nearest Neighbors algorithm so that you can communicate its intuition to beginners who are just starting out using the Rubix ML library. You'll also have the freedom to use your creativity to develop a guide that is delightful and conveys the fundamentals of supervised classification, cross-validation, and data extraction.
Take a look at the other tutorials as a guide for structure and depth. The target audience is a beginner and the goal is to get them up and running quickly but leave breadcrumbs of information for them to explore later.
One suggestion is to include somewhere an intuitive explanation of KNN where the dataset is imagined in some n-dimensional Euclidean space and the classifier is locating nearest samples in such a space.
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