Rms error of regression

What Rms error of regression is

Root Mean Square Error (RMSE) is a measure of the difference between values predicted by a model or an estimator and the values observed. It is a commonly used metric in regression models as it can be used to compare different models.

Steps for calculating RMS Error of Regression:

  1. Calculate the residuals for each data point by subtracting the predicted value from the observed value.
  2. Square each of the residuals.
  3. Sum the squared residuals.
  4. Divide the sum of the squared residuals by the number of data points.
  5. Take the square root of the result to get the Root Mean Square Error (RMSE).

Examples

  1. A scientist is analyzing the relationship between temperature and humidity. To assess the accuracy of the linear regression model predicting humidity from temperature, the scientist can use the RMS error of regression.

  2. A financial analyst is predicting stock prices using a linear regression model. To gauge the accuracy of the model, she can use the RMS error of regression.

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