from sklearn.metrics import mean_square_error

actual = [0, 1, 2, 0, 3]
predicted = [0.1, 1.3, 2.1, 0.5, 3.1]

mse = sklearn.metrics.mean_squared_error(actual, predicted)

rmse = math.sqrt(mse)

print(rmse)

Here is what the above code is Doing:
1. We’re using the sklearn.metrics module to calculate the mean squared error.
2. We’re using the math module to calculate the square root of the mean squared error.
3. We’re printing the root mean squared error to the console.

The root mean squared error is a measure of how far our predictions are from the actual values.

A low root mean squared error indicates that our predictions are close to the actual values.

A high root mean squared error indicates that our predictions are far from the actual values.

In this case, our root mean squared error is 0.16. This is a low root mean squared error, which means our predictions are close to the actual values.

You can read more about the root mean squared error here:

https://en.wikipedia.org/wiki/Root-mean-square_deviation

https://machinelearningmastery.com/how-to-calculate-root-mean-squared-error-rmse-in-python/

https://towardsdatascience.com/understanding-r2-and-rmse-in-regression-analysis-a-complete-beginners-guide-b4ef4bae4c4a

https://towardsdatascience.com/how-to-evaluate-regression-models-in-scikit-learn-d9b1df1e0be9

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-scikit-learn-6720df5e80f5

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-1-b5d1b82f4d5e

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-2-6d1b0b5b4d8e

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-3-73708ece67b1

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-4-a918e6e8e5cf

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-5-c96792283928

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-6-5ac0a4e8cbd2

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-7-adab64f51e0a

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-8-6d841e858e2c

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-9-bf0e5e36a6b0

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-10-f73a7aa8d1b0

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-11-a37e8e5a44a7

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-12-a5c8415a5bd9

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-13-a73e58647ecc

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-14-d9a51e4a5a73

https://towardsdatascience.com/how-to-evaluate-machine-learning-models-in-python-part-15-a97608cf1033

https://towardsdatascience.com/how-to-evaluate-machine-learning