Скачать или смотреть Short Primer on Tree-Based Machine Learning Models

Dive into the world of tree-based models in machine learning with our detailed video guide. We unravel the intricacies of decision trees, random forests, and gradient boosting machines (GBMs), illustrating their functionality and unique advantages in AI decision-making processes. Through practical Python examples using the Iris dataset, we demonstrate how these models operate, from visualizing decision trees to implementing random forests and GBMs.

Additionally, we tackle common challenges such as overfitting, handling continuous variables, and dealing with large, complex datasets, offering effective solutions and tips.

Whether you're a beginner or an experienced practitioner, this video is designed to enhance your understanding and skills in machine learning. Join us on this journey to master tree-based models, apply your knowledge to real-world data projects, and advance your career in the ever-evolving field of data science.

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Timeline:

Intro: (0:00)
Decision Trees (0:20)
Random Forest: (1:43)
GBMs: (2:46)
Issues and solutions when implementing decision trees (4:15)
Conclusion: (7:23)

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