Concept

Cross-validation (statistics)

Related lectures (32)

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Explores hydroacoustic simulation parameters, mass oscillation modeling, and validation errors in hydroelectric installations.

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Delves into model evaluation, covering theory, training error, prediction error, resampling methods, and information criteria.

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Machine Learning Fundamentals: Regularization and Cross-validation

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Model Complexity and Overfitting in Machine Learning

Covers model complexity, overfitting, and strategies to select appropriate machine learning models.

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Explores polynomial curve fitting, kernel functions, and regularization techniques, emphasizing the importance of model complexity and overfitting.

Overfitting, Cross-validation & Regularization

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Careful Cross-validation

Emphasizes the significance of careful cross-validation in deep neural networks, including the split of data and the concept of K-fold cross-validation.

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Using cross-validation: Building a final predictor

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