The data science questions are automatically evaluated by using accuracy measures. The commonly used measures are as follows:

- Root-mean-square error
- Mean absolute error

**Root-mean-square error**

It is a frequently used measure of the differences between predicted outcomes and observed outcomes. The root-mean-square deviation represents the square root of the second sample moment of the differences between predicted values and observed values or the quadratic mean of these differences. These deviations are called residuals when the calculations are performed over the sample data set and are called errors (or prediction errors) when the computed value is beyond the sample data set. This technique is mainly used in climatology, forecasting, and regression analysis to verify experimental results.

The RMSE formula is as follows:

where

**f**denotes the expected values**o**denotes the observed values

**Mean absolute error**

In this technique, the amount of error in predicted outcomes and observed outcomes. Here, the absolute value of the errors is considered valid for the calculation.

To determine the absolute error (Δx), you must use the following formula:

**(Δx) = x _{i} – x**

where

**x**denotes the predicted outcome_{i}**x**denotes the observed outcome

The mean absolute error** **(MAE) is the average of all the calculated absolute errors. The formula is:

where

**n**denotes the number of errors**Σ**(summation symbol) denotes to add all the absolute errors**|x**denotes the absolute errors_{i}– x|

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