A linear regression model is able to predict the output accurately for a given set of inputs. What underlying factor is primarily responsible for the model's performance in terms of prediction accuracy?

The main reason a linear regression model predicts accurately is that its estimated coefficients correctly capture the true linear relationship between the input features and the output. When the data truly follows a linear pattern and the model’s weights are close to the real underlying parameters, the predictions will be close to the actual values. In other words, the model’s performance depends on how well the fitted line represents the underlying trend in the data. For example, if house price depends linearly on square footage, a regression that learns the right slope and intercept will give accurate price predictions.