What is meant by ‘Training set’ and ‘Test Set’?

In the context of machine learning, both the training set and the test set are essential components used in the development and evaluation of predictive models.

1. Training Set: The training set is a subset of data used to train a machine learning model. It consists of input-output pairs, where the inputs are the features or attributes of the data, and the outputs are the corresponding labels or target values that the model aims to predict. During the training phase, the model learns patterns and relationships within the training data to make predictions. The model’s parameters or weights are adjusted iteratively based on the training data until it can accurately predict the target variable.
2. Test Set: The test set is another subset of data that is separate from the training set. It serves as an independent dataset used to evaluate the performance of the trained model. The test set contains unseen instances that the model has not been exposed to during training. By evaluating the model on the test set, we can assess its generalization ability, i.e., how well it performs on new, unseen data. This evaluation helps determine if the model has learned meaningful patterns from the training data or if it has overfit (memorized noise) and cannot generalize to new instances.

In summary, the training set is used to train the model, while the test set is used to evaluate its performance and generalization ability. It’s crucial to keep these datasets separate to ensure unbiased evaluation and to avoid data leakage, where information from the test set inadvertently influences the training process. Additionally, techniques like cross-validation can be employed to further validate the model’s performance using different subsets of the data.