How do different machine learning algorithms compare in terms of performance?

How do different machine learning algorithms compare in terms of performance?

The performance of machine learning algorithms can vary based on the dataset and the problem type. Common metrics include accuracy, precision, recall, and F1 score.

Which algorithms are generally considered the best for classification tasks?

For classification tasks, algorithms like Random Forest, Support Vector Machines, and Gradient Boosting are often top performers.

What factors should I consider when choosing an algorithm?

Consider factors like the size of your dataset, the complexity of the problem, interpretability, and computational resources.

Can you explain the difference between overfitting and underfitting?

Overfitting occurs when a model learns the training data too well, capturing noise, while underfitting happens when it fails to capture the underlying trend.