This is the same as Mean Squared Error (MSE) but the root of the value is considered while determining the accuracy of the model. import numpy as np import sklearn.metrics as metrics actual = np.array([56,45,68,49,26,40,52,38,30,48]) predicted = np.array([58,42,65,47,29,46,...
The Root-Mean-Square Error (RMSE) is one of the methods to determine the accuracy of our model in predicting the target values. In machine Learning when we want to look at the accuracy of our model we take the root mean square of the error that has occurred between the test values and...
1. What is the purpose of Mean Squared Error (MSE) in machine learning? MSE measures the average difference between predicted and actual values. 2. How do you calculate Root Mean Squared Error (RMSE)? RMSE is the square root of the average squared difference between predicted ...