Advantages and Limitations of MSE
The advantages and limitations of mean squared error is mentioned below:
Advantages
- Provides the comprehensive measure of the model accuracy.
- Sensitive to the both large and small errors.
- Easy to the calculate and interpret.
Limitations
- It can be heavily influenced by the outliers.
- It penalizes large errors disproportionately which may not always be desirable.
Mean Squared Error
Mean Squared Error (MSE) is a fundamental concept in statistics and machine learning playing a crucial role in the assessing the accuracy of the predictive models. It measures the average squared difference between predicted values and the actual values in the dataset. This article aims to provide a comprehensive overview of the mean squared error, its significance in statistical analysis, and its applications in various domains.