Mean Squared Error (MSE) is a statistical metric used to evaluate the accuracy of a model’s predictions by quantifying the difference between predicted values and the actual values observed in the data.
The formula for calculating MSE is:
MSE = (1/n) * Σ(actual – predicted)²
Here, n is the number of observations, actual represents the true values, and predicted are the values generated by the model. The squared differences are used to ensure that positive and negative errors do not cancel each other out, emphasizing larger errors more than smaller ones.
MSE is widely used in regression analysis and machine learning to assess how well a model performs. A lower MSE value indicates better model performance, as it signifies that the predictions are closer to the actual values. Conversely, a higher MSE indicates larger errors and poorer model accuracy.
While MSE is a useful metric, it is important to note that it is sensitive to outliers due to the squaring of errors. Therefore, in cases where the data may contain outliers, other metrics like Mean Absolute Error (MAE) might be considered for evaluation.
In summary, Mean Squared Error is a fundamental concept in predictive modeling, providing a clear numeric value that reflects the quality of a model’s predictions.