training error
What is training error?
Training error is a critical metric in the AI and machine learning industry, as it provides insight into how well a model has learned from the training dataset. It is calculated by comparing the model's predictions against the actual values in the training data and measuring the discrepancies. A lower training error generally indicates that the model has effectively captured the patterns in the data. However, it's essential to balance training error with validation error to ensure the model generalizes well to new, unseen data. Overfitting occurs when a model has a very low training error but performs poorly on new data, indicating it has learned the noise in the training data rather than the underlying patterns. Thus, while a low training error is desirable, it must be considered alongside other metrics to ensure robust model performance.
Training error is the error rate or the measure of inaccuracy that an artificial intelligence (AI) model exhibits on the same dataset that was used to train it.
Examples
- An image recognition model trained to identify cats and dogs shows a training error of 5%. This means that out of every 100 images from the training set, the model misclassifies 5 images.
- A spam detection algorithm trained on a dataset of emails has a training error of 2%. This indicates that for every 100 emails in the training set, the model incorrectly labels 2 emails as either spam or not spam.
Additional Information
- Training error is one of the first indicators of how well a model is performing during the training phase.
- It's important to compare training error with validation error to detect overfitting or underfitting in the model.