This document compares support vector machine (SVM) and decision tree models for predicting California's ecoregions based on weather data. Both models were optimized and evaluated using cross-validation. The SVM achieved a higher overall accuracy of 84.7% compared to the decision tree's 65.3%. However, the decision tree may be more intuitive. The models could help assess climate change risk and find similar climates elsewhere.