In Sample Error
In Sample Error, also known as training error, is a measure of how well a machine learning model fits the data it was trained on. It quantifies the discrepancy between the model's predictions and the actual outcomes within the training dataset. This metric is used to assess model performance during the training phase, but it can be misleading if used alone for generalization evaluation.
Developers should learn about In Sample Error to understand model fitting and avoid overfitting, where a model performs well on training data but poorly on unseen data. It is crucial in machine learning workflows for initial model validation, hyperparameter tuning, and comparing different algorithms during development. Use cases include evaluating regression or classification models in supervised learning tasks, such as predicting house prices or customer churn.