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Root Mean Squared Error (RMSE): |

## Root Mean Squared Error Formula

The formula for root mean square error (RMSE) is as follows:

RMSE = √[ Σ(Pi – Ai)2 / n ]

where:

- RMSE = root mean square error

Σ = summation operator

Pi = predicted value for the ith observation

Ai = observed value(Actual Value) for the ith observation

n = number of observations

## How to calculate RMSE?

The RMSE is calculated by taking the square root of the average of the squared differences between the predicted values (Pi) and the actual values (Ai), for a set of data points.

To calculate the Root Mean Square Error (RMSE), follow these steps:

- First, you’ll need a dataset with the actual values (observed values) of the dependent variable (Ai) and the predicted values (Pi) from your mode
- Calculate the Squared Differences = (Pi – Ai)2
- Sum the Squared Differences = Σ(Pi – Ai)2
- Calculate the Mean Squared Error (MSE) = Σ(Pi – Ai)2 / n
- Finally, take the square root of the MSE to get the Root Mean Square Error (RMSE).

## What is Root Mean Square Error?

Root Mean Square Error (RMSE) is a commonly used metric to evaluate the accuracy of a predictive model, particularly in the context of regression analysis or forecasting. It measures the difference between the actual observed values and the values predicted by the model.

A lower RMSE indicates that the model is better at predicting the observed values. A higher RMSE indicates that the model is not as good at predicting the observed values.