Abstract

Machine learning is a statistical and computational approach to extracting important patterns and trends in data. This entry is an overview of machine learning methods for social science research. It covers supervised learning methods including generalized linear models, support vector machines, naive Bayes, k-nearest neighbor, artificial neural networks and deep learning, decision trees, and ensemble methods. It also notes several important considerations relevant to supervised learning algorithms including the use of training and test data and cross-validation, loss optimization and evaluation metrics, bias-variance trade-off, and overfitting and regularization strategies. The entry also covers unsupervised learning methods, including k-means clustering, hierarchical clustering, network community detection, principal component analysis, and t-distributed stochastic neighbor embedding. A section on text analysis incorporates supervised and unsupervised learning of documents and neural networks. The entry provides an overview of new developments at the intersection of machine learning methods and causal inference. Key limitations and considerations for adopting these methods in empirical social science research conclude the entry.