An international research team has utilized a machine learning algorithm known as XGBoost to predict PV adoption among homeowners. This algorithm consists of a distributed gradient-boosted decision tree (GBDT) machine learning library that can help accurately predict a target variable by combining an ensemble of estimates from a set of simpler and weaker models. The researchers compared the performance of the proposed algorithm with the logistic regression approach and found that the adapted algorithm was able to offer better results than the logistic regression in predictive performance. The superior performance of the machine learning-based approach was attributed to its ability to integrate complex variable interactions and nonlinearity.
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