Types of Datasets Used in AI Training
In order to train artificial intelligence (AI), you typically need three types of datasets: a training dataset, a validation dataset, and a test dataset.
Training Dataset
The training dataset is the initial data the AI learns from.
For example, let's say you are developing an AI to distinguish between cats and dogs. The training dataset would include a multitude of cat and dog images, each clearly labeled as either a cat or a dog. The AI uses this data to learn the distinguishing features of cats and dogs.
The training dataset constitutes the largest portion of the entire dataset. The performance of the AI is heavily dependent on the quantity and quality of this dataset. Therefore, how you construct the training dataset significantly affects the performance of the AI.
Validation Dataset
The validation dataset is used to evaluate the AI's performance during the training process. It's akin to working on practice problems while studying for a test.
The validation dataset is separate from the training dataset and is used to identify any areas where the AI may be learning incorrectly, or to check for issues like overfitting or underfitting.
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Overfitting: When a model is tailored too closely to the training data, making it difficult to generalize to new data.
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Underfitting: When a model hasn't learned enough from the training data to accurately generalize to new data.
It's crucial to avoid overfitting and ensure that the AI model is not overly specialized in the training data but can also handle new, unseen data effectively.
Test Dataset
The test dataset is used to evaluate how well the AI performs in real-world situations. Think of it as the final exam after completing your study.
Typically, the test dataset is composed of data entirely different from the training and validation datasets. This setup ensures that the AI is evaluated based on how well it can handle completely new, unseen data.
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