Introduction
The accuracy of a prediction model is assessed using the Mean Squared Error (MSE), a metric that is frequently used in the fields of statistics and machine learning. It calculates the mean squared difference between the values in the dataset that are actual and those that are predicted. The MSE is a useful metric for assessing the quality of regression models since a lower MSE indicates a better fit of the model to the data.
Example
In this practical example, we assess the regression model’s precision using the mean square error (MSE) measure to estimate property values based on square footage. We determined the squared error for each observation using a data set that contained five data points with the true value and the matching estimate by taking the square of the difference between the predicted value and the true value. Finally, we get the MSE by averaging these squared errors. The determined MSE of 440,000 in this scenario provides a quantitative assessment of how well the model tracks actual home price trends, with lower values suggesting a better fit. This graph demonstrates how MSE can be used to estimate and fit regression models for precise predictions.