Training History: Tracking Model Performance during Machine Learning Training
“History” in the context of machine learning usually refers to the information about the performance of a trained model during training.
When a machine learning model is trained, the model learns from the training data and tries to minimize a loss function that measures the error between predicted and true outputs. During the training process, the model’s performance on the training data is monitored, and the optimizer adjusts the model’s parameters to improve its performance on the training data.
Training usually involves iterating over multiple epochs (passes through the training data). During each epoch, the optimizer updates the model’s parameters based on the gradients of the loss function concerning the parameters. At the end of each epoch, the model’s performance on the training data and sometimes the validation data is evaluated and recorded.
The “history” object in machine learning libraries such as Keras and TensorFlow contains this performance information, including the loss and accuracy of the model on the training and validation data during each epoch. It can be used to visualize the training process, identify overfitting, and make decisions about model selection and hyperparameter tuning.
