среднеквадра-тичная погрешность in python

To calculate the root mean square error or "среднеквадра-тичная погрешность" in Python, you can use the math and statistics modules.

Here's an example code snippet that calculates the root mean square error:

main.py
import math
import statistics

# true values
true_values = [2, 4, 6, 8]

# predicted values
predicted_values = [1, 3, 5, 7]

# square the differences between predicted and true values
squared_errors = [(true - pred)**2 for true, pred in zip(true_values, predicted_values)]

# calculate the mean of squared errors
mse = statistics.mean(squared_errors)

# calculate the root of mean squared errors
rmse = math.sqrt(mse)

print("Root mean squared error:", rmse)
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This code first defines the true values and the predicted values as two lists. It then calculates the squared errors between the predicted and true values, followed by calculating the mean squared errors using the statistics.mean() function.

Finally, it calculates the root mean squared error by taking the square root of the mean squared errors using the math.sqrt() function.