How can I evaluate a linear regression model using NumPy?

How can I evaluate a linear regression model using NumPy?

You can evaluate a linear regression model by calculating metrics like Mean Squared Error (MSE) and R-squared. Would you like to see an example?

Yes, please provide an example of calculating MSE.

Sure! After fitting your model, you can calculate MSE using:

import numpy as np

# Assuming y_true are the actual values and y_pred are the predicted values
mse = np.mean((y_true - y_pred) ** 2)
print('Mean Squared Error:', mse)

What about R-squared? How do I calculate that?

R-squared can be calculated using the formula:

ss_total = np.sum((y_true - np.mean(y_true)) ** 2)
ss_residual = np.sum((y_true - y_pred) ** 2)
rsq = 1 - (ss_residual / ss_total)
print('R-squared:', rsq)

Thanks! Are there any other evaluation metrics I should consider?

Yes, you might also consider metrics like Mean Absolute Error (MAE) and Adjusted R-squared for a more comprehensive evaluation.