How can I explain the interpretation of the root mean squared error?
I have been asked to present the results of a machine learning model that can predict house prices based on various features such as size, location, and number of bedrooms. How can I explain the interpretation of the RMSE(Root mean squared error) to a group of stakeholders who are not familiar with machine learning terminology and why is it important in evaluating the accuracy of the prediction of the model?
In the context of data science, the root mean squared error is a commonly used metric to measure the average deviation of the predicted values from the actual values in a regression problem like as a prediction of the prices of houses. It is calculated by taking the square root of the Average of the squared differences between predicted and actual values.
For example, let’s say you have a regression model for predicting house prices:-
From sklearn.metrics import mean_squared_error
Import numpy as np
# Assuming y_true and y_pred are the actual and predicted house prices respectively
Mse = mean_squared_error(y_true, y_pred)
Rmse = np.sqrt(mse)
Print(“RMSE:”, rmse)