Loss and accuracy:
In the context of the LSTM training method, "loss" and "accuracy" serve as metrics to evaluate the performance of the model during training and validation. Let's break down what each means:
Binary Cross-Entropy Loss
This loss function evaluates the agreement between predicted probabilities and actual binary class labels in a classification task. In binary classification scenarios, the binary cross-entropy loss is applied. It’s a method for measuring the discrepancy between predicted class probabilities and the actual labels, which are 0 or 1. The loss increases when the predicted probability strays from the actual label, and decreases when the prediction aligns more closely with the label.
Within the code, the instruction model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) indicates the selection of binary cross-entropy for the model’s loss function.
Accuracy Metric
Accuracy represents the fraction of predictions the model gets right and is calculated as:
Accuracy = (Number of Correct Predictions) / (Total Number of Predictions)
This metric is a straightforward way to gauge the model’s effectiveness, presenting an easily understandable success rate. Nonetheless, it might not be the most reliable indicator of model quality, especially in cases where the data classes are not evenly distributed.