How do different machine learning algorithms compare?

How do different machine learning algorithms compare?

There are several machine learning algorithms, each with its strengths and weaknesses. Are you interested in supervised or unsupervised algorithms?

I'm more interested in supervised algorithms. Can you compare decision trees and support vector machines?

Sure! Decision trees are easy to interpret and visualize, while support vector machines are effective in high-dimensional spaces. However, SVMs can be more complex to tune.

What about their performance on large datasets?

Decision trees can overfit on large datasets, while SVMs can handle large datasets better but may require more computational resources.

Which one would you recommend for a beginner?

I would recommend starting with decision trees due to their simplicity and ease of understanding.