Glossary¶

Use this page to record terms and ideas that help you understand professional analytics projects.

This project covers regression: building and evaluating models that predict a continuous numeric value from input features.

Pro-tip: Expand the VS Code Outline view (below the navigator on the right) to see this file organization at-a-glance.

Regression Evaluation¶

residual¶

A residual is the difference between a model's predicted value and the true value. Examining residuals reveals where a model is consistently over- or under-predicting.

MAE (Mean Absolute Error)¶

MAE is the average of the absolute differences between predicted and true values. It is easy to interpret: an MAE of 5 means predictions are off by 5 units on average.

RMSE (Root Mean Squared Error)¶

RMSE is the square root of the average squared differences between predicted and true values. It penalizes large errors more heavily than MAE. A lower RMSE means predictions are closer to the true values.

R-squared (R²)¶

R-squared measures how much of the variance in the target the model explains. A value of 1.0 means perfect predictions; a value of 0.0 means the model does no better than predicting the mean every time. Negative values are possible when the model performs worse than the mean.

baseline model¶

A baseline model is the simplest possible prediction strategy, such as always predicting the mean of the training target. A good model should outperform the baseline; if it does not, the features or approach need revisiting.

Regression Models¶

linear regression¶

Linear regression fits a straight line (or hyperplane) through the data to predict a continuous target. It is fast and interpretable but assumes a linear relationship between features and target.

decision tree regressor¶

A decision tree regressor predicts a continuous value by partitioning feature space into regions and predicting the mean of each region. It can capture nonlinear relationships but may overfit.