# AI-900: Regression Model Residuals: Ideal Clustering for Accurate Predictions

Discover where residual values should cluster in a regression model’s histogram for precise predictions. Learn how a clustering around zero signifies accurate model predictions with low RMSE.

## Question

You create a regression model with low RMSE and review the best model metrics.

Where on the Residual histogram should the most frequently occurring residual values cluster for your model?

A. 1
B. 0.5
C. 0
D. -1
E. 2
F. -0.5

C. 0

## Explanation

In a regression model with low Root Mean Squared Error (RMSE), the most frequently occurring residual values should ideally cluster around 0 on the Residual histogram. This clustering indicates that the model’s predictions are close to the actual values, minimizing errors.

The Residual histogram presents the frequency of residual values distribution. Residual is the difference between predicted and actual values. It represents the amount of error in the model.

If we have a good model, we should expect that most of the errors are small. They will cluster around 0 on the Residual histogram.

c 350 300 250 200 150 100 50 Residual Histogram Residuals 6%.16

All other options are incorrect.

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### Alex Lim

Alex Lim is a certified IT Technical Support Architect with over 15 years of experience in designing, implementing, and troubleshooting complex IT systems and networks. He has worked for leading IT companies, such as Microsoft, IBM, and Cisco, providing technical support and solutions to clients across various industries and sectors. Alex has a bachelor’s degree in computer science from the National University of Singapore and a master’s degree in information security from the Massachusetts Institute of Technology. He is also the author of several best-selling books on IT technical support, such as The IT Technical Support Handbook and Troubleshooting IT Systems and Networks. Alex lives in Bandar, Johore, Malaysia with his wife and two chilrdren. You can reach him at [email protected] or follow him on Website | Twitter | Facebook