regression

name	regression
description	Build and evaluate regression models to predict a target variable. Use when modeling relationships between features and an outcome.
allowed-tools	mcp__localdata__describe_database mcp__localdata__execute_query mcp__localdata__analyze_regression mcp__localdata__evaluate_model_performance
argument-hint	<database-name> <target-column>

Regression

Build, evaluate, and compare regression models to predict a target variable from features.

Steps

1. Parse arguments. Extract the database name and target column from $ARGUMENTS. The first argument is the database name, the second is the target variable to predict.

2. Explore available features. Call describe_database with the database name. Identify numeric columns as potential predictors. Note the target column's distribution (continuous vs discrete) to confirm regression is appropriate.

3. Extract and inspect data. Call execute_query to select the target column and candidate feature columns. Check for nulls, outliers, and sufficient row count (at least 30 observations per feature as a rule of thumb).

4. Build a linear baseline. Call analyze_regression with the database name, target column, feature columns, and model type "linear". Review the results: R-squared, adjusted R-squared, coefficients, p-values, and residual diagnostics.

5. Evaluate baseline performance. Call evaluate_model_performance to get detailed metrics: RMSE, MAE, R-squared, and residual analysis. Assess whether the linear model captures the relationship adequately.

6. Interpret baseline results. Check for:

   • R-squared below 0.3: poor fit, consider non-linear models
   • Features with p-values above 0.05: candidates for removal
   • Multicollinearity: correlated predictors inflating variance
   • Residual patterns: non-random residuals suggest model misspecification

7. Try alternative models if needed. If the linear baseline performs poorly (R-squared below 0.5 or patterned residuals), call analyze_regression with alternative model types:

   • "ridge" for multicollinearity issues
   • "lasso" for feature selection (many predictors)
   • "polynomial" for curved relationships

8. Compare models. For each model fitted, compare R-squared, RMSE, and MAE. Select the model with the best balance of performance and interpretability. Simpler models are preferred when performance is comparable.

9. Present the final model. Report:

   • Selected model type and rationale
   • Key performance metrics (R-squared, RMSE, MAE)
   • Most important features and their coefficients
   • Practical interpretation of the model equation
   • Limitations and assumptions

10. Recommend next steps. Suggest validating on held-out data, running /localdata-mcp:analyze-correlations to find additional predictors, or using /localdata-mcp:cluster-analysis to identify subgroups where the model performs differently.