📊✨ Welcome to our comprehensive RU-vid series on mastering data imbalance! Join us on an enlightening journey through the top 5 techniques for achieving optimal balance in your machine learning models.
1. Oversampling & Under sampling Mastery: Achieving Class Harmony
Discover the art of handling imbalanced datasets with precision! Uncover the power of Oversampling the Minority Class and strategic Under sampling of the Majority Class. Learn how these foundational techniques enhance predictive accuracy, fortify model robustness, and lay the groundwork for reliable machine learning models.
2. SMOTE Mastery: Transforming Imbalanced Datasets with Precision
Embark on a transformative journey into the realm of data science with "SMOTE Mastery." Uncover the secrets behind the Synthetic Minority Over-sampling Technique (SMOTE), a cutting-edge method for achieving optimal data balance. See how SMOTE seamlessly integrates into our overarching strategy for creating harmonious datasets, mitigating bias, and enhancing model generalization.
3. Data Harmony Unveiled: K-Fold Cross Validation for Perfect Balance
Dive into the powerful technique of K-Fold Cross Validation, a game-changer in achieving perfect balance in your machine learning models. Explore how this technique optimizes predictions, fine-tunes model performance, and contributes to the overarching goal of achieving optimal balance in your machine learning endeavours.
4. Ensemble Mastery: Achieving Data Balance with BalancedRandomForest!
Delve into the world of ensemble techniques, focusing on BalancedRandomForest to achieve data balance in machine learning models. Learn the fundamentals, implement step-by-step using Python and scikit-learn, and explore the impact on model performance through real-world examples and metrics.
🚧 Up Next: Mastering Practical Usage of Machine Learning
Stay tuned for our next tutorial where we'll guide you through the practical side of machine learning. Learn how to save your trained models for future use, discover the power of automation in data preprocessing, and master practical tips for efficient model sharing and collaboration.
🔍 Why It Matters:
Balancing data is the foundation for building reliable machine learning models. Join us as we unravel the intricacies of these techniques, mitigating bias, enhancing generalization, and crafting models that excel in real-world scenarios.
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Resources -
Data Balancing & Model Building - github.com/LEA...
Data used in balancing work - github.com/LEA...
Data used to test the model - github.com/LEA...
Output of the prediction - github.com/LEA...
Data Balancing & Model Building - github.com/LEA...
Saved Ensemble Model - github.com/LEA...
Saved SMOTE Logistic Model - github.com/LEA...
Data Pre-Prep. Automated Module - github.com/LEA...
Code Created in the video - github.com/LEA...
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#DataBalancing #MachineLearning #DataScience #SMOTE #KFold #BalancedRandomForest #Automation #Visualization #ModelSaving #AI #MLTips #DataHarmony
29 сен 2024
