Machine Learning Regression (MLR) diagnostics
This module provides various tools to create and evaluate MLR models for arbitrary input variables.
Examples
Constraining uncertainty in projected gross primary production (GPP) with machine learning: Use Gradient Boosted Regression Tree (GBRT) algorithm to constrain projected Gross Primary Production (GPP) in RCP 8.5 scenario using observations of process-based predictors.
Diagnostic scripts
Auxiliary scripts
Available MLR models
- Gradient Boosted Regression Trees (sklearn implementation)
- Gradient Boosted Regression Trees (xgboost implementation)
- Gaussian Process Regression (sklearn implementation)
- Huber Regression
- Kernel Ridge Regression
- LASSO Regression
- LASSO Regression with built-in CV
- LASSO Regression (using Least-angle Regression algorithm) with built-in CV
- Linear Regression
- Random Forest Regression
- Ridge Regression
- Ridge Regression with built-in CV
- Support Vector Regression