Machine Learning Feature Selection for Climate Model Validation

Create a sophisticated feature selection and dimensionality reduction framework for climate science machine learning models. The system should dynamically evaluate thousands of environmental variables, identifying statistically significant predictors while mitigating multicollinearity. Implement at least three different feature selection algorithms (e.g., mutual information, recursive feature elimination, LASSO regularization) and develop a comparative visualization dashboard that explains variable importance and interdependencies.