Read Aloud the Text Content

This audio was created by Woord's Text to Speech service by content creators from all around the world.

Text Content or SSML code:

<speak> Welcome to the lesson on model evaluation.<break strength="x-strong"/> The last step is the model evaluation. <break strength="strong"/>We will see different error metrics for the trained model. <break strength="strong"/>We have trained the model and want to check how it is performing. <break strength="strong"/>We want to evaluate whether it is predicting accurately or not. <break strength="strong"/>There are different error metrics like mean square error, <break strength="weak"/>mean absolute error, <break strength="weak"/>and root mean squared error. First, <break strength="weak"/>we will check the root mean squared error.<break strength="x-strong"/> Now we are importing mean squared error. <break strength="strong"/>The most commonly used metric is the root mean squared error. <break strength="strong"/>So we are importing mean squared error from the metrics class of sci-kit-learn library. <break strength="x-strong"/> What is root mean squared error (RMSE)?<break strength="x-strong"/> Root mean squared error (RMSE) is <break strength="weak"/>the square root of the average of the square of all of the errors.<break strength="x-strong"/> The output is stored in the pred_train variable. <break strength="strong"/>The sqrt method is useful to find the square root. <break strength="x-strong"/> The sqrt method is available in the NumPy library. <break strength="x-strong"/> We find the square root of the average of the square of all of the errors.<break strength="strong"/> train_y is the actual values of the output for the input. <break strength="x-strong"/> pred_ train is the predicted value from the model. <break strength="x-strong"/> That means the difference between predicted and actual value will be squared <break strength="strong"/>and sum together and take the square root. <break strength="x-strong"/> We are getting an error of 0.056 error for training and validation.<break strength="x-strong"/> For the testing data, we are getting an error of 0.062.<break strength="strong"/> For the training and testing data, <break strength="weak"/>we are getting enough scores. <break strength="strong"/>So our model is performing well. <break strength="x-strong"/> </speak>