How Gradient Boosting REALLY Works in Machine Learning
This video dives deep into the mechanics of gradient boosting in machine learning. Using a mock dataset inspired by the Dunning-Kruger effect, we explain how gradient boosting builds sequential models, reduces residuals, and improves accuracy with each iteration. From simple averages to decision trees, discover how this powerful method transforms predictions in real-world applications.
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*Chapters:*
00:00 Introduction to Gradient Boosting
00:35 Dunning-Kruger Effect: Mock Dataset Explained
01:37 Gradient Boosting Process Overview
02:03 Model 1: Simple Average Prediction
02:46 Residuals and Model 2: First Decision Tree
03:43 Refining Predictions with Model 3
04:43 Model 4: Increasing Complexity
05:20 Combining Models for Final Predictions
06:22 Practical Applications and Realistic Scenarios
06:56 Conclusion and Next Steps
#GradientBoosting #MachineLearning #XGBoost #MLTutorial #AIExplained #DataScience #BoostingModels #PredictiveModeling #MLConcepts #DecisionTrees #ErrorReduction #MLTips #GradientBoostingIntuition #AIModels #SuperDataScience
The video is about gradient boosting, a powerful machine learning technique used to improve predictive models. It walks through the process of how gradient boosting works, starting with an explanation of the concept using a mock dataset inspired by the Dunning-Kruger effect, a psychological phenomenon. The video demonstrates how gradient boosting builds sequential models, each focusing on correcting the errors (residuals) of the previous model. It explains:
- The initial model as a simple average prediction.
- How residuals are calculated and used to train subsequent decision trees.
- How each iteration reduces errors and improves the model's accuracy.
- The final ensemble model as a combination of all previous models.
The video emphasizes the intuition behind gradient boosting, the role of residuals, and the iterative nature of the process, making it accessible for beginners and valuable for those looking to refine their understanding of machine learning.
Видео How Gradient Boosting REALLY Works in Machine Learning канала Super Data Science
*Course Link HERE:* https://sds.courses/ml-2
*You can also find us here:*
Website: https://www.superdatascience.com/
Facebook: https://www.facebook.com/groups/superdatascience
Twitter: https://twitter.com/superdatasci
LinkedIn: https://www.linkedin.com/company/superdatascience/
Contact us at: support@superdatascience.com
*Chapters:*
00:00 Introduction to Gradient Boosting
00:35 Dunning-Kruger Effect: Mock Dataset Explained
01:37 Gradient Boosting Process Overview
02:03 Model 1: Simple Average Prediction
02:46 Residuals and Model 2: First Decision Tree
03:43 Refining Predictions with Model 3
04:43 Model 4: Increasing Complexity
05:20 Combining Models for Final Predictions
06:22 Practical Applications and Realistic Scenarios
06:56 Conclusion and Next Steps
#GradientBoosting #MachineLearning #XGBoost #MLTutorial #AIExplained #DataScience #BoostingModels #PredictiveModeling #MLConcepts #DecisionTrees #ErrorReduction #MLTips #GradientBoostingIntuition #AIModels #SuperDataScience
The video is about gradient boosting, a powerful machine learning technique used to improve predictive models. It walks through the process of how gradient boosting works, starting with an explanation of the concept using a mock dataset inspired by the Dunning-Kruger effect, a psychological phenomenon. The video demonstrates how gradient boosting builds sequential models, each focusing on correcting the errors (residuals) of the previous model. It explains:
- The initial model as a simple average prediction.
- How residuals are calculated and used to train subsequent decision trees.
- How each iteration reduces errors and improves the model's accuracy.
- The final ensemble model as a combination of all previous models.
The video emphasizes the intuition behind gradient boosting, the role of residuals, and the iterative nature of the process, making it accessible for beginners and valuable for those looking to refine their understanding of machine learning.
Видео How Gradient Boosting REALLY Works in Machine Learning канала Super Data Science
gradient boosting XGBoost Dunning-Kruger effect predictive modeling machine learning data science decision trees residuals ML tutorial boosting intuition error reduction sequential modeling ML models boosting methods gradient boosting process AI explained practical ML boosting algorithms data processing decision tree predictions hyperparameter tuning ML techniques gradient boosting tutorial boosting simplified ML beginners model refinement. AI
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29 января 2025 г. 16:00:04
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