I'm training an unsupervised machine learning model and want to make sure my features are as useful as possible!
Do unsupervised machine learning model featured need to be independent? For example, I have a feature (subscriptionId) that is the subscription Id of different cloud accounts within a Tenant. I also have a feature that is the resourceId of a resource within the subscription.
However, this resourceId contains the subscriptionId. Is it best practice to combine these features or remove one feature (e.g. subscriptionId) to avoid dependence and duplication among dataset features?
CodePudding user response：
For unsupervised learning, commonly used for clustering, association, or dimensionality reduction, features don't need to be fully independent, but if you have many unique values it's likely that your models can learn to differentiate on these high entropy values instead of learning interesting or significant things as you might hope.
If you're working on generative unsupervised models, for customers, I cannot express how much risk this may create, for security and secret disclosure, for Oracle Cloud Infrastructure (OCI) customers. Generative models are premised on regurgitating their inputs, and thousands of papers have been written on getting private information back out of trained models.
It's not clear what problem you're working on, and the question seems early in its formulation.
I recommend you spend time delving into the limits of statistics and data science, which are the foundation of modern popular machine learning methods.
Once you have an idea of what questions can be answered well by ML, and what can't, then you might consider something like fastAI's course.
https://www.nature.com/articles/nmeth.4642
Again, depending on how the outputs will be used or who can view or (even indirectly) query the model, it seems unwise to train on private values, especially if you want to generate outputs. ML methods are only useful if you have access to a lot of data, and if you have access to the data of many users, you need to be good steward of Oracle Cloud customer data.
CodePudding user response：
Unsupervised learning is intrinsically more difficult than supervised learning as it does not have corresponding output. The result of the unsupervised learning algorithm might be less accurate as input data is not labeled, and algorithms do not know the exact output in advance.