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This paper trains a household-level disaster risk classifier based on supervised machine learning algorithms for cold wave-related disasters. The households' features considered for this task proxy multiple dimensions of vulnerability to disasters accounting for economic, health, social, and geographical conditions. These features are theoretically hypothesized to explain disaster risk classification. We test our predictive model based on the case of Puno, Peru, where cold wave-related disasters (e.g., −28°in 2003 and -35° in 2004) are recurrent and overwhelming. Two supervised learning algorithms were tested to build the classifiers: Logistic Regression and Random Forest Classifier. Hyperparameters of such models were optimized through Bayesian Optimization heuristic. Random Forest Classifier outperformed Logistic Regression by 1.16 % in MCC and 2.34 % in Sensitivity. In the test dataset, Random Forest …