Monitoring and interpreting train and test losses are fundamental aspects of training and evaluating machine learning models. Below are metrics that guide the iterative process of model development, helping practitioners build models that generalize well to real-world scenarios.
1. Loss Function:
- A loss function, also known as a cost or objective function, quantifies the difference between the predicted values of a machine learning model and the actual target values. The goal during training is to minimize this loss, indicating that the model’s predictions are closer to the actual outcomes.
2. Training Phase:
- During the training phase, the model is exposed to a dataset with known input-output pairs. The loss is computed for each prediction, and the model adjusts its internal parameters (weights and biases) to minimize the cumulative loss across the entire dataset.
3. Train Loss:
a. Definition:
- The train loss, often referred to as the training loss or training error, represents the error or difference between the predicted output and the actual target values during the training phase of a machine learning model.
