predictive-modeling-best-practices

What problem does it solve?

Defines and enforces rigorous predictive modeling workflows for ecological data, ensuring robust validation, responsible feature selection, and reproducible analyses.

Core Features & Use Cases

• Cross-Validation & Spatial CV Guidance: Specifies appropriate validation strategies for SDMs and ecology models, including block CV.
• Collinearity & Feature Selection: Provides protocols for diagnosing predictor redundancy (VIF, pairwise correlations) and ecologically informed selection.
• Hyperparameter Tuning & Leakage Audits: Guides grid searches, regularization choices, and checks to prevent data leakage.
• Reproducibility & Reporting: Outputs modeling plans, evaluation metrics, and documentation for reproducible workflows.

Quick Start

Analyze ecological predictor data and prepare a modeling plan by applying rigorous modeling best practices to your dataset in R or Python.