Root mean squared error

As explained in Investing for Programmers

Root mean-squared error is the square root of the average squared difference between predicted and observed values.

How Investing for Programmers covers it

How deep it goes

Defines RMSE and demonstrates its calculation, but stops short of deriving the formula or analyzing metric assumptions.

Examples

Demonstrates RMSE with AAPL historical prices, next-day labels, a random forest, and actual-versus-forecast plots.

What it assumes

Assumes basic supervised learning, labeled datasets, feature selection, and training-test splitting.

What you can do after

Apply RMSE via NumPy and scikit-learn to validate a trained price-forecasting model on test data.

How it connects

Connects RMSE to supervised learning, train-test splits, random forests, ARIMA, and forecast visualization.

liveBookmanning.com

Read Investing for Programmers on liveBook Get the book at manning.com

Also explained in

• 

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

  liveBookmanning.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.
• 

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

  liveBookmanning.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.
• 

  liveBookmanning.com
  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIRoot mean squared error is the square root of the mean squared differences between predicted and observed numeric values.
• 

  liveBookmanning.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 assigns values to candidate solutions, predictions, or parameter settings according to a criterion to be minimized or maximized.
• 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 Investing for Programmers. The section links point to the parts of the book it draws on.