Root mean squared error

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover root mean squared error?

Defines the metric and applies it within pipelines and cross-validation, but does not derive its formula mathematically.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Root mean squared error is the square root of the mean squared differences between predicted and observed numeric values.

Root mean squared error (RMSE) measures the typical size of numeric prediction errors by taking the square root of their mean squared differences. Lower values indicate smaller prediction errors. section 4.3

Use RMSE to make regression evaluation consistent and comparable:

Place the data transformations and regression model in one pipeline. section 4.3
Measure RMSE with the same cross-validation strategy used across candidate models. section 4.3
Report the average and standard deviation of the cross-validated RMSE to assess consistency across samples. section 4.3

Comparing Airbnb price models

For the Tokyo Airbnb problem, XGBoost achieved an RMSE of 10,783.027, while fastai achieved 11,719.387. Alongside R-squared and MAE, these values show that XGBoost produced the stronger overall result, with RMSE about 8% to 9% lower. section 12.4

Table 12.4 Comparison of results from XGBoost and fastai models (view table figure)

Metric	XGBoost	Fastai
R-squared	0.599	0.572
RMSE	10783.027	11719.387
MAE	6531.102	7152.143

Compare RMSE, MAE, and R-squared values for the XGBoost and fastai solutions in a compact results table. section 12.4

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

liveBook manning.com

Read Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Grokking Machine LearningA regression metric equal to the square root of the average squared difference between observed and predicted target values.
liveBook manning.com

Starting Data Analytics with Generative AI and PythonRoot mean squared error is the square root of mean squared error and expresses prediction error in the dependent variable's units.
liveBook manning.com

Effective Data AnalysisRoot mean squared error is the square root of the mean squared differences between predicted and actual numerical values.
liveBook manning.com

Demand Forecasting Best PracticesRoot mean squared error is the square root of the mean of the squared differences between forecast values and corresponding actual values.
liveBook manning.com

Investing for ProgrammersRoot mean-squared error is the square root of the average squared difference between predicted and observed values.
liveBook manning.com

AI-Powered SearchRoot mean square error is the square root of the mean squared difference between predicted and actual values.

Compare how 7 books explain root mean squared error →

Related concepts

mean squared erroruses · Mean squared error is the arithmetic mean of the squared differences between corresponding predicted and target numerical values.
forecast erroruses · A forecast error is the difference between an observed value and the corresponding forecasted value.
linear regressionmeasures · Linear regression is a statistical modeling method that estimates a continuous response as a linear combination of predictor variables and fitted coefficients.
language modelmeasures · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
objective functionkind of · An objective function is a function or criterion that assigns values to candidate solutions or parameter settings and is optimized by minimizing or maximizing those values.
squared lossused by · Squared loss is the square of the difference between a predicted or fitted value and its target or observation, often summed across data points.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.