Скачать или смотреть Understanding Random Forests: How Ensemble Trees Work and Their Uses

In this video we demystify Random Forest, a powerful ensemble technique that improves the stability and accuracy of decision‑tree models. You’ll learn how a forest is built from many bootstrap‑sampled trees, why each split considers only a random subset of features, and how the collective voting (for classification) or averaging (for regression) reduces overfitting. Visual explanations illustrate the growth of a single decision tree, the introduction of randomness, and the impact of aggregating hundreds of trees. Practical examples—including a house‑price prediction scenario—show how the method balances bias and variance in real‑world data. The tutorial also highlights common tools such as scikit‑learn for implementing and tuning Random Forest models. By the end of the video you’ll understand the core concepts, the workflow for building a forest, and the typical applications of this versatile algorithm.