Compute Mean Square Error (MSE) for a Problem

In summary, the conversation is about computing the mean square error (MSE) for a problem. The process involves estimating a variable using realizations of random variables. To get accurate results, the estimation process needs to be repeated with different seeds. The output of each trial is denoted as x_i. The question is how to compute the MSE using the true value of x. Equation (1) is the correct formula, while equation (2) could lead to inaccurate results.
  • #1
kasraa
16
0
Hi all,

I want to compute mean square error (MSE) for a problem but I'm not sure if I'm doing it right.

Suppose that I want to estimate a variable (e.g. the position of an object) like x. The estimation process depends on the realizations of some specific random variables (i.e. Gaussian noises). In order to get accurate results, I know that I have to perform the estimation process N times with different seeds (i.e. different realizations of noises), right?

Lets show the output (the estimated position) of the i'th trial with x_i.

So I have x_1,...,x_N. Assume that we have access to the true value of x which is showed by x_(true).

How should I compute the MSE?

(1) [tex]\frac{1}{N} \sum_{i=1}^{N} (x_{true} - x_i)^2[/tex]

or

(2) [tex](x_{true} - (\frac{1}{N} \sum_{i=1}^{N} x_i))^2[/tex]

Many thanks.
 
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  • #2
Equation (1) is the correct formula. One problem with equation (2), which indicates how wrong it can be, is that you could have a sample set with an average equal to the true mean, but with wildly fluctuating terms, leading to zero as your variance estimate.
 
  • #3
Thanks for your help.
 

Related to Compute Mean Square Error (MSE) for a Problem

1. What is Mean Square Error (MSE)?

Mean Square Error (MSE) is a commonly used metric in machine learning and statistics to measure the average squared difference between the actual and predicted values of a variable. It is used to evaluate the performance of a model and is an important tool for assessing regression models.

2. How is MSE calculated?

MSE is calculated by taking the average of the squared differences between the actual and predicted values. This is done by summing up the squared differences for each data point and then dividing by the total number of data points.

3. What does a high MSE value indicate?

A high MSE value indicates that the model's predictions are far from the actual values, which means that the model is not performing well. This could be due to underfitting or poor data quality.

4. Is a lower MSE always better?

Yes, a lower MSE value indicates that the model is predicting the values more accurately. However, it is important to note that MSE should not be the only factor used to evaluate a model, as it can be influenced by outliers and the scale of the data.

5. How can MSE be used to compare different models?

MSE can be used to compare the performance of different models on the same dataset. The model with the lower MSE value is generally considered to be better at predicting the values. However, it is important to use other metrics and techniques to evaluate the models as well.

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