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Root Mean Squared Error (RMSE) — Machine Learning Zoomcamp Module 2

In the previous lesson we found out our predictions were a bit off from the actual target values in the training dataset. We need a way to quantify how good or bad the model is. This is where RMSE can be of help. Root Mean Squared Error...

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Machine Learning Zoomcamp › Module 2: Machine Learning for Regression › Root Mean Squared Error (RMSE)

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In the previous lesson we found out our predictions were a bit off from the actual target values in the training dataset. We need a way to quantify how good or bad the model is. This is where RMSE can be of help.
Root Mean Squared Error (RMSE) is a way to evaluate regression models. It measures the error associated with the model being evaluated. This numerical figure can then be used to compare models, enabling us to choose the one that gives the best predictions.

\[RMSE = \sqrt{ \frac{1}{m} \sum_{i=1}^{m} {(g(x_i) - y_i)^2}}\]

$g(x_i)$ is the prediction
$y_i$ is the actual value
$m$ is the number of observations in the dataset (i.e. cars)

The entire code of this project is available in this jupyter notebook.

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Notes from Peter Ernicke

DataTalks.Club

Root Mean Squared Error (RMSE) — Machine Learning Zoomcamp Module 2

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DataTalks.Club

Root Mean Squared Error (RMSE) — Machine Learning Zoomcamp Module 2

Related Pages

Notes

Key concepts

Related notes

Sources