What are the advantages of decision forests over other algorithms?

What are the advantages of decision forests over other algorithms?

Decision forests, or random forests, offer several advantages: they handle large datasets well, reduce overfitting, and provide feature importance.

How do they compare in terms of accuracy?

In many cases, decision forests achieve high accuracy due to their ensemble nature, averaging multiple decision trees to improve predictions.

Are there situations where other algorithms perform better?

Yes, in cases with very small datasets or when interpretability is crucial, simpler models like logistic regression might perform better.

What about computational efficiency?

Decision forests can be computationally intensive, especially with large datasets, while algorithms like linear regression are generally faster.

Can you give examples of real-world applications?

Sure! Decision forests are used in finance for credit scoring, in healthcare for disease prediction, and in marketing for customer segmentation.