Question

How can I evaluate the performance of a regression model using metrics like mean squared error (MSE) and R-squared?

Answer 1

MSE and R-squared are common metrics for evaluating regression models. Here's an example code snippet to compute these metrics using scikit-learn:

from sklearn.metrics import mean_squared_error, r2_score

# Assuming `y_true` and `y_pred` are the true and predicted values, respectively
mse = mean_squared_error(y_true, y_pred)
r2 = r2_score(y_true, y_pred)

print("Mean Squared Error (MSE):", mse)
print("R-squared:", r2)