What are some common mistakes in AI model evaluation?
An example of an AI chatbot conversation about common AI model evaluation mistakes.
What are some common mistakes in AI model evaluation?
Some common mistakes include not using a diverse dataset, overfitting, and ignoring evaluation metrics.
Can you explain why overfitting is a problem?
Overfitting occurs when a model learns the training data too well, failing to generalize to new data.
What evaluation metrics should I focus on?
Focus on metrics like accuracy, precision, recall, and F1 score, depending on your specific use case.
How can I avoid these mistakes in my evaluation process?
To avoid mistakes, ensure you use cross-validation, a diverse dataset, and regularly review your metrics.
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