What are the disadvantages of supervised learning?

What are the disadvantages of supervised learning?

Supervised learning has several disadvantages, including the need for large labeled datasets, which can be time-consuming and expensive to obtain.

Are there any issues with overfitting?

Yes, overfitting is a common problem in supervised learning where the model learns the training data too well, including noise, leading to poor generalization on new data.

How does it compare to unsupervised learning?

Unlike supervised learning, unsupervised learning does not require labeled data, making it more flexible but potentially less accurate for specific tasks.

Can you give examples of real-world applications?

Sure! Supervised learning is used in applications like spam detection, image recognition, and medical diagnosis, but each has its challenges.

What are some alternatives to supervised learning?

Alternatives include unsupervised learning, reinforcement learning, and semi-supervised learning, each with its own advantages and use cases.