Regression Model Building Plan for LLM Evals

Overview

This project implements a complete pipeline for building and evaluating regression models, with a focus on best practices for LLM evaluation tasks. The pipeline includes data ingestion, cleaning, model building, and comprehensive evaluation using industry-standard metrics.

Detailed Implementation Steps

1. Data Ingestion from CSV Files

• Objective: Load real-world datasets relevant to LLM evaluation tasks
• Implementation:
  • Use pandas read_csv() to load multiple CSV files
  • Handle different file encodings and delimiters
  • Combine multiple datasets if needed
  • Validate data structure and basic statistics
• Best Practices:
  • Check for file existence before loading
  • Handle large files with chunked reading if necessary
  • Log data loading process

2. Data Cleaning using Pandas

• Objective: Prepare data for modeling by handling missing values, outliers, and inconsistencies
• Implementation:
  • Handle missing values (drop, fill with mean/median/mode, interpolation)
  • Detect and treat outliers using statistical methods (IQR, Z-score)
  • Data type conversions and categorical encoding
  • Feature scaling/normalization if required
  • Remove duplicates and irrelevant columns
• Best Practices:
  • Document all cleaning decisions
  • Use pipelines for reproducible preprocessing
  • Validate data integrity after cleaning

3. Model Building with Scikit-Learn

• Objective: Train regression models using scikit-learn
• Implementation:
  • Split data into train/validation/test sets (80/10/10 or cross-validation)
  • Implement multiple regression algorithms:
    • Linear Regression (baseline)
    • Ridge/Lasso Regression (regularization)
    • Random Forest Regressor
    • Gradient Boosting (XGBoost, LightGBM)
    • Support Vector Regression
  • Hyperparameter tuning using GridSearchCV or RandomizedSearchCV
  • Feature selection techniques
• Best Practices:
  • Use stratified sampling if applicable
  • Implement early stopping for iterative models
  • Save trained models using joblib/pickle

4. Model Evaluation with Top Metrics and Best Practices

• Objective: Comprehensive evaluation focusing on LLM eval best practices
• Key Metrics:
  • R-squared (R²): Proportion of variance explained
  • Mean Absolute Error (MAE): Average absolute prediction error
  • Mean Squared Error (MSE): Average squared prediction error
  • Root Mean Squared Error (RMSE): Square root of MSE
  • Mean Absolute Percentage Error (MAPE): Percentage error
  • Explained Variance Score: How well variance is explained
• Best Practices for LLM Evals:
  • Cross-Validation: K-fold CV to ensure robustness
  • Train-Test Split: Prevent data leakage
  • Residual Analysis: Check model assumptions
  • Feature Importance: Understand model decisions
  • Model Comparison: Compare multiple algorithms
  • Performance Visualization: Learning curves, prediction vs actual plots
  • Error Distribution Analysis: Check for bias
  • Confidence Intervals: For predictions in production
  • A/B Testing Framework: For comparing model versions
  • Monitoring and Drift Detection: For real-time deployment

Usage

1. Place your CSV files in the data/raw/ directory
2. Run the Jupyter notebook notebooks/regression_pipeline.ipynb
3. Review evaluation results in results/ directory

Notebook for learning

notebooks/regression_pipeline.ipynb

Report

Generated evaluation report is here - results/evaluation_report.md

FastAPI Response

Simple API to get the data and feed to UI for my testing purpose - api/README.md

LLM Eval Specific Considerations

• Focus on regression tasks common in LLM evaluation (e.g., quality scoring, performance prediction)
• Ensure evaluation metrics align with business objectives
• Implement continuous monitoring for model performance in production
• Consider fairness and bias evaluation for LLM outputs

Project Structure

llm-evals-for-regression/
├── data/
│   └── raw/          # Raw CSV files
├── notebooks/
│   └── regression_pipeline.ipynb  # Main implementation notebook
├── src/
│   ├── data_ingestion.py
│   ├── data_cleaning.py
│   ├── model_building.py
│   └── evaluation.py
├── models/           # Saved trained models
├── results/          # Evaluation results and plots
├── requirements.txt
└── README.md

Dependencies

• pandas
• numpy
• scikit-learn
• matplotlib
• seaborn
• jupyter
• xgboost (optional)
• lightgbm (optional)