What does the training phase of an AI model refer to?

a) The process of making predictions on new data
b) The phase where the model learns from historical data
c) The process of evaluating the model’s accuracy
d) The phase where the model is deployed to users

Correct Answer

b) The phase where the model learns from historical data

Explanation

• b) The phase where the model learns from historical data: The “training” phase is when the AI model learns patterns from the provided data. During training, the model is fed with a dataset, and it adjusts its parameters (like weights in a neural network) to minimize error or loss. This helps the model to make accurate predictions or decisions based on the learned patterns in the data. Essentially, it’s the learning phase.

Incorrect Answers

• a) The process of making predictions on new data: This refers to the “inference” or “testing” phase, where a trained model is used to make predictions on unseen data. After the model is trained, it is evaluated on new data to check how well it generalizes to real-world scenarios, but this is not part of the training phase.

• c) The process of evaluating the model’s accuracy: This happens after the training phase, during the validation or testing phase. After the model is trained, its accuracy or performance is evaluated using a separate dataset (often called the test set) to see how well it performs on data it hasn’t seen before.

• d) The phase where the model is deployed to users: This is the “deployment” phase, which occurs after the training and evaluation phases. Once the model is trained and tested, it can be deployed for real-world use, where it can make predictions for users in production environments.

In summary, training is the phase where the AI model learns patterns from historical or training data to make accurate predictions.