Cross Validated Asked by Soulduck on November 2, 2021
I’m newbie in AI
I know that Supervised Learning algorithms are divided into Classification and Regression algorithms.
But is that true of all machine learning algorithms, not just Supervised Learning? Are there any other categories than Classification and Regression?
First you need to know that machine learning algorithms are broadly divided into three categories-
But you should know that most production level machine learning pipelines use a combination of two or all of the three kinds of algorithms.
Supervised Learning takes advantage of already known labels, like whether an email is reported spam or not, how much rainfall has occured in the last 7 days, whether a lump in body is carcinogenic or not etc.
Where in Unsupervised Learning, the data is not labeled i.e. there are no clearly defined target variables (nature of email, amount of rainfall and nature of tumor are the target variables in the previous cases).
Reinforcement Learning algorithms are complex and advanced where the model learns from its previous predictions and correctness.
So, whenever there is a clearly defined target variable, you can apply a supervised learning algorithm. Regression and Classification fall into the supervised learning domain, and cannot be classified as unsupervised learning models.
And, there are many supervised learning algorithms which are not regression or classification, for example-
etc.
These are just some examples of the supervised learning algorithms. And these, along with regression and classification, do not fall under unsupervised learning algorithms. Some of the most common unsupervised learning algorithms are-
etc.
Here's a diagram-
Machine Learning Algorithms
|
|
---------------------------------------------------------------------------------
| | |
supervised learning unsupervised learning reinforcement learning
| |
|--->Naive Bayes Classifier |--->Clustering
|--->Support Vector Machine |--->Neural Networks
|--->Decision Tree |--->Anomaly Detection
|--->Random Forest
|--->Regression
|--->Classification
These questions are better suited for the Data Science Stack Exchange site.
Answered by truth on November 2, 2021
Generally speaking "supervised" learning", "classification" and "regression" are actually very different levels of meaning.
Supervised learning is a high level categorization of ML problems which defines all challenges where we have at least some solved/labeled data. This is opposed to unsupervised learning (we don't know the solution) and reinforcement learning (data and labels are generated procedurally).
Classification is specific goal of ML which you can compare to targets like prediction, outlier detection, dimension reduction, etc.
Finally regression is a specific mathematical algorithm which can help us achieve tasks and might be opposed to algorithms such as a Neural Net, Naive Bayes, etc.
A specific ML model can be described in all three terms:
An unsupervised classification problem solved with a K-Means clustering algorithm
A supervised prediction problem solved with a linear regression
A reinforcement learning optimization problem solved with a monte carlo model.
Answered by Fnguyen on November 2, 2021
All unsupervised algorithms, e.g.
Some of them might internally use regression or classification elements, but the algorithm itself is neither.
Answered by Michael M on November 2, 2021
No, it's much broader than that. You should at least read about the following:
Answered by gunes on November 2, 2021
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