Mastering Random Forest: A Deep Dive with Gradient Boosting Comparison

Author(s): Kuriko Iwai

Originally published on Towards AI.

Explore architecture, optimization strategies, and practical implications

Ensemble methods are common techniques in machine learning.

This article dives into the Random Forest algorithm, exploring its fundamental architecture and performance metrics compared to Gradient Boosting Machines (GBMs). It discusses the methodology behind ensemble learning, the mechanics of tree construction, and the importance of hyperparameter tuning. Key concepts include boosting samples, voting mechanisms, and the practical implications of model complexity on predictive performance. The author provides insights into evaluating model efficacy and comparing Random Forest with GBMs, ultimately concluding that while Random Forest offers robust predictions, it comes with certain computational challenges in large empirical datasets.

Read the full blog for free on Medium.

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Published via Towards AI