How do I evaluate a linear regression model?
An example of an AI chatbot conversation about evaluating linear regression models.
How do I evaluate a linear regression model?
To evaluate a linear regression model, you can use metrics like R-squared, Mean Absolute Error (MAE), and Mean Squared Error (MSE).
What does R-squared tell me about my model?
R-squared indicates the proportion of variance in the dependent variable that can be explained by the independent variables. A higher R-squared value means a better fit.
Can you explain Mean Absolute Error?
Mean Absolute Error (MAE) measures the average magnitude of errors in a set of predictions, without considering their direction. It’s the average over the test sample of the absolute differences between prediction and actual observation.
What are some common pitfalls in evaluating linear regression?
Common pitfalls include overfitting, ignoring multicollinearity, and relying solely on R-squared without considering other metrics.
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