Description: Residuals in the context of supervised learning refer to the differences between observed values and values predicted by a model. In other words, they are the prediction errors that a model makes when trying to estimate an outcome based on a dataset. These residuals are fundamental for evaluating the accuracy and effectiveness of a machine learning model. A positive residual indicates that the model has underestimated the actual value, while a negative residual suggests that it has overestimated the value. Analyzing residuals allows researchers and developers to identify patterns in prediction errors, which can lead to improvements in the model. Additionally, residuals are used in various evaluation metrics, such as mean squared error (MSE) and mean absolute error (MAE), which help quantify the quality of predictions. In summary, residuals are a key tool in supervised learning, as they provide valuable information about model performance and guide the optimization process.
