A training dataset is a collection of data used to teach a machine learning model how to make predictions or decisions. This dataset contains input data and corresponding output labels, allowing the model to learn patterns and relationships within the data. For example, in image recognition, the training dataset might include pictures of cats and dogs, along with labels identifying each image. The quality and size of the training dataset significantly impact the model's performance. A well-structured dataset helps the model generalize better to new, unseen data. In contrast, a small or biased dataset can lead to poor predictions and overfitting, where the model performs well on training data but fails on real-world data.