Model Validation

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Model validation ensures that an AI system can make accurate predictions in real-world situations without simply memorizing its training data. It involves testing the model on separate validation datasets that it hasn’t seen before and measuring how well it performs using metrics like accuracy, precision, and recall.

This process helps reveal whether the model truly understands patterns in the data or is just overfitting – performing well on known examples but failing on new ones. It can also show signs of underfitting, where the model hasn’t learned enough to make reliable predictions. By validating models before deployment, developers can ensure the AI behaves consistently, making the final system more trustworthy and effective in practice.

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