Visualize The Performance Of Any Linear Regression Model With This Simple Plot

Assumption turned into performance validation.

Linear regression assumes that the model residuals (=actual-predicted) are normally distributed.

If the model is underperforming, it may be due to a violation of this assumption.

A residual distribution plot is a great way to verify this and also determine the model's performance.

As the name suggests, it depicts the distribution of residuals (=actual-predicted).

A good residual plot will:

• Follow a normal distribution

• NOT reveal trends in residuals

A bad residual plot will:

• Show skewness

• Reveal patterns in residuals

Thus, the more normally distributed the residual plot looks, the more confident you can be about your model.

This is especially useful when the regression line is difficult to visualize, i.e., in a high-dimensional dataset.

So remember...

After running a linear model, always check the distribution of the residuals.

This will help you:

• Validate the model's assumptions

• Determine how good your model is

• Find ways to improve it (if needed)

👉 Over to you: What are some other ways/plots to determine the linear model's performance?

Thanks for reading!

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.