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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.
b. Purpose:
- The goal during training is to minimize this loss. It serves as a measure of how well the model is learning the patterns in the training data. The loss is typically calculated using a loss function that quantifies the disparity between predicted and actual values.
c. Optimization:
- The training process involves adjusting the model’s parameters (weights and biases) iteratively to minimize the training loss. Techniques such as gradient descent are commonly employed to find the optimal model parameters that result in the lowest training loss.
d. Monitoring:
- Monitoring the training loss is crucial to assess the model’s progress. As the model learns from the data, the training loss ideally decreases, indicating improved performance.
