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The video tutorial discusses the concept of overfitting in machine learning, explaining how models can become too tailored to training data, leading to poor performance on new data. It also covers strategies to address overfitting, ensuring models generalize well to unseen data.

Can you provide an example of a situation where overfitting might occur?

What role does the training dataset size play in overfitting?