Model Validation vs  Model Evaluation

Purpose

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Validation: Involves splitting data into training and validation sets. Evaluation: Uses separate test data after fully training the model.

Process

Validation: Involves splitting data into training and validation sets.   Evaluation: Uses separate test data after fully training the model.

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Timing

Validation: Done during the model development phase.   Evaluation: Conducted after the model is trained and tuned.

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Focus

Validation: Focuses on fine-tuning model parameters.   Evaluation: Focuses on assessing the final model's accuracy and reliability.

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Validation: Techniques like k-fold cross-validation are used.   Evaluation: Techniques like confusion matrix, ROC curves, and accuracy metrics are used.

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Techniques

Iteration

Validation: Iterative process to improve model performance.   Evaluation: Typically, a one-time process to finalize the model's effectiveness.

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Outcome

Validation: Results guide model adjustments and enhancements.    Evaluation: Results determine if the model is ready for deployment.

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