Unsupervised learning can be really helpful in some tough situations. Here are a few reasons why it might be better than supervised learning:
Not Enough Labeled Data: Getting data that is labeled (like saying what each piece of data means) can cost a lot of money and take a lot of time. Unsupervised methods can find patterns in data that isn’t labeled. This helps solve the problem of not having enough labeled data.
Finding Hidden Patterns: Sometimes we don’t know what the data looks like on the inside. Unsupervised learning helps us explore and discover these hidden patterns. But, it can sometimes be hard to understand what we find.
Handling Big Data: Unsupervised techniques can work with large amounts of data. However, they might not do as well if the settings of the algorithm aren’t set up correctly.
To tackle these challenges, using strong evaluation methods and mixing unsupervised with supervised methods can lead to better results.
Unsupervised learning can be really helpful in some tough situations. Here are a few reasons why it might be better than supervised learning:
Not Enough Labeled Data: Getting data that is labeled (like saying what each piece of data means) can cost a lot of money and take a lot of time. Unsupervised methods can find patterns in data that isn’t labeled. This helps solve the problem of not having enough labeled data.
Finding Hidden Patterns: Sometimes we don’t know what the data looks like on the inside. Unsupervised learning helps us explore and discover these hidden patterns. But, it can sometimes be hard to understand what we find.
Handling Big Data: Unsupervised techniques can work with large amounts of data. However, they might not do as well if the settings of the algorithm aren’t set up correctly.
To tackle these challenges, using strong evaluation methods and mixing unsupervised with supervised methods can lead to better results.