What are the main challenges of supervised learning in AI?

What are the main challenges of supervised learning in AI?

Some key challenges include overfitting, underfitting, data quality, and the need for large labeled datasets.

Can you explain overfitting and why it's a problem?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, leading to poor performance on new data.

How can we prevent overfitting in our models?

Techniques like cross-validation, regularization, and using simpler models can help prevent overfitting.

What role does data quality play in supervised learning?

Data quality is crucial; poor quality data can lead to inaccurate models, as the model learns from the data it is given.

Are there any alternatives to supervised learning?

Yes, alternatives include unsupervised learning, semi-supervised learning, and reinforcement learning, each suited for different tasks.