Training Data

What is training data in AI?

Training data in AI refers to the data used to train a machine learning model. The training data consists of input data and corresponding output data, which the model uses to learn the underlying patterns and relationships. The quality and quantity of the training data can significantly affect the performance of the model.

Why is training data important?

Training data is important because it is the basis for the learning process of a machine learning model. The model learns to make predictions by finding patterns in the training data. If the training data is representative of the problem space and includes a variety of scenarios, the model is likely to generalize well to new, unseen data.

However, if the training data is unrepresentative or biased, the model may perform poorly or make biased predictions.

How is training data used in AI?

In supervised learning, the most common type of machine learning, training data is used to teach the model the correct output for a given input. The model makes predictions based on the input data and adjusts its parameters based on the difference between its predictions and the actual output in the training data. This process is repeated many times until the model's predictions are as close as possible to the actual outputs.

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