Regression Ml

ML Regression

Purpose

Build regression models using machine learning algorithms for problems where linear regression is insufficient.

How It Works

Step 1: Baseline

• Simple linear regression as baseline metric
• Evaluate: RMSE, MAE, R², MAPE

Step 2: Train ML Models

• Elastic Net: Regularized linear (handles multicollinearity)
• Random Forest: Non-linear, robust, feature importance
• XGBoost/LightGBM: Best accuracy on tabular data
• SVR: Kernel-based, good for small datasets
• Cross-validation with shuffled or time-based splits

Step 3: Evaluate

• Metrics: RMSE, MAE, MAPE, R² on holdout set
• Residual analysis: patterns, heteroscedasticity
• Prediction intervals (quantile regression or conformal prediction)
• Learning curves for bias-variance diagnosis

Step 4: Interpret

• Feature importance (SHAP, permutation, gain)
• Partial dependence plots
• Residual vs. predicted plots

Usage Examples

"Predict monthly revenue using marketing spend, seasonality, and market data"

"Build a house price estimator with prediction intervals"

Output Format

• Model Comparison: Metrics table across models
• Best Model: Hyperparameters and performance metrics
• Feature Importance: SHAP summary plot
• Predictions: With confidence/prediction intervals
• Python Code: sklearn / XGBoost pipeline

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: ericwang915
• Source: ericwang915/data-scientist-skills
• License: MIT

Install and usage instructions live in the source repository linked above.