Скачать или смотреть Master K-Fold Cross-Validation for Machine Learning

This video covers K-Fold Cross-Validation, a critical method for assessing machine learning models. Learn how to split your data into multiple folds, train on subsets, and validate models effectively. This technique ensures reliable metrics and helps prevent overfitting, making it essential for robust model evaluation.

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Chapters:
00:00 Introduction to K-Fold Cross-Validation
00:34 Schools of Thought on Testing
01:05 Splitting Data for Validation
01:34 How K-Fold Cross-Validation Works
02:34 Rotating Validation Folds
04:03 Aggregating Metrics for Reliability
05:03 Refining Models with Cross-Validation
06:05 Alternative Approaches to K-Fold
07:42 Combining Traditional and K-Fold Validation
08:43 Avoiding Data Leakage
09:06 Conclusion and Best Practices

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The video explains *K-Fold Cross-Validation*, a crucial technique in machine learning for evaluating model performance. It focuses on:

Purpose: K-Fold Cross-Validation ensures reliable metrics by splitting data into multiple folds, training on subsets, and validating on unseen data, reducing the risk of overfitting and reliance on a single test set.
Process: The training data is divided into K folds (e.g., 10). The model trains on K-1 folds while the remaining fold is used for validation. This process repeats K times, rotating the validation fold each time.
Aggregated Metrics: The results from each fold are combined to assess the model's overall performance, providing a more robust evaluation than traditional train-test splits.
Schools of Thought: The video discusses different approaches, including using K-Fold Cross-Validation with or without a separate test set.
Refinement: If the aggregated metrics are unsatisfactory, hyperparameters or the model itself can be adjusted and re-evaluated.
Variations: Alternative workflows, such as using K-Fold as a supplementary validation method after a traditional train-test split, are also explored.
Key Considerations: Emphasizes avoiding data leakage and the importance of consistent hyperparameters across folds.
This video provides a detailed, beginner-friendly overview of K-Fold Cross-Validation, making it a valuable resource for improving model validation workflows in machine learning.