Machine Learning Feature Selection for Climate Model Prediction

Create a robust feature selection and dimensionality reduction pipeline for climate prediction models that handles multi-dimensional geospatial datasets with high correlation and sparse measurements. Develop an automated technique that can: 1) Detect and handle missing data, 2) Implement advanced feature ranking algorithms like mutual information and recursive feature elimination, 3) Generate interpretable model complexity metrics, and 4) Support multiple machine learning backends (scikit-learn, TensorFlow).