What is the difference between Training Loss Validation Loss and Evaluation Loss

In the context of machine learning, particularly in supervised learning, “training loss,” “validation loss,” and “evaluation loss” are terms used to describe different types of loss functions that are used during the training and evaluation of a model. Let’s define each term:

Training Loss:

• The training loss is a measure of how well a machine learning model is performing on the training data.
• It is calculated during the training process and is used to update the model’s parameters through techniques like gradient descent to minimize this loss.
• The training loss reflects how well the model is fitting the training data. It should decrease over time as the model learns from the data.
• However, a very low training loss doesn’t necessarily mean the model will perform well on new, unseen data, as it may have overfit the training data.
• In a classification problem, a common loss function is the cross-entropy loss (also known as log loss or logistic loss).
• For a single data point (i), where the true label is denoted as y_i (0 or 1 for binary classification, or a one-hot encoded vector for multi-class classification) and the predicted probability of the positive class is denoted as p_i, the cross-entropy loss can be expressed as:

L_train(i) = -[y_i * log(p_i) + (1 — y_i) * log(1 — p_i)]

• The training loss (L_train) is typically computed as the average of this loss over all training data points:

L_train = (1/N_train) * Σ L_train(i) for i in [1, N_train]

Validation Loss:

• The validation loss is a measure of how well a machine learning model is performing on a separate dataset called the validation set.
• During training, a portion of the data (not used for training) is set aside as the validation set.
• The model’s performance on the validation set is evaluated periodically (after each epoch, for example), and the validation loss is calculated.
• The validation loss helps assess how well the model generalizes to data it hasn’t seen during training.
• It is used to monitor the model’s performance and to detect overfitting. If the validation loss starts increasing while the training loss is decreasing, it’s a sign of overfitting.
• Similar to the training loss, the validation loss is computed using the cross-entropy loss function.
• For each data point in the validation set, you calculate the loss in the same way as for the training data.
• The validation loss (L_validation) is computed as the average of the losses over all validation data points:

L_validation = (1/N_validation) * Σ L_validation(i) for i in [1, N_validation]

Evaluation Loss (Test Loss)

• The evaluation loss, sometimes referred to as the test loss, is a measure of the model’s performance on a completely separate dataset that it has never seen before, known as the test set.
• The test set is used to provide an unbiased estimate of the model’s generalization performance.
• After the model has been trained and its hyperparameters have been tuned based on the training and validation performance, it is evaluated on the test set to assess how well it will perform in real-world scenarios.
• The evaluation loss on the test set helps determine the model’s readiness for deployment and provides an estimate of its expected performance on unseen data.
• The evaluation loss, often referred to as the test loss, is calculated using the same loss function (cross-entropy) as the training and validation losses.
• For each data point in the test set, you calculate the loss in the same way as for training and validation.
• The evaluation loss (L_evaluation or L_test) is computed as the average of the losses over all test data points:

L_evaluation = (1/N_test) * Σ L_evaluation(i) for i in [1, N_test]

In summary, training loss is used to optimize the model’s parameters during training, validation loss helps monitor the model’s performance during training and detect overfitting, and evaluation loss on the test set provides an unbiased estimate of the model’s generalization performance on unseen data. It’s crucial to keep these different types of losses separate to ensure that the model’s performance is accurately assessed and that it can make good predictions on new, real-world data.