In machine learning, the term "test loss" typically refers to the performance of a trained model on a separate dataset that it has not seen during the training phase. The loss is a measure of how well the model is performing, where the goal is often to minimize this loss.

Here's a breakdown of the key concepts:

• Loss Function: During the training of a machine learning model, a loss function is used to quantify how well the model's predictions match the actual target values. The goal is to minimize this loss, indicating that the model is making accurate predictions.

• Training Loss: The loss calculated on the training dataset during the training process. The model adjusts its parameters (weights and biases) based on this loss to improve its performance.

• Test Loss (or Validation Loss): After training the model, it is essential to evaluate its performance on a separate dataset that it has not seen before, commonly referred to as the test set or validation set. The loss calculated on this independent dataset is known as the test loss. It provides an indication of how well the model generalizes to new, unseen data.

A low training loss doesn't guarantee good generalization, as the model might have memorized the training data (overfitting) and may not perform well on new data. The test loss helps assess the model's ability to generalize by evaluating its performance on data it hasn't encountered during training.

Monitoring both training and test losses is crucial in machine learning to strike a balance between model complexity and generalization. If the training loss continues to decrease while the test loss increases, it may indicate overfitting. Conversely, if both training and test losses are high, it may suggest underfitting, indicating that the model hasn't learned the underlying patterns in the data effectively.