Tunneling-induced ground settlement is influenced by a variety of features. In this article, a machine learning model is proposed to predict the ground settlement induced by shield tunneling. A hybrid feature selection method based on the importance degree of variables is first used to select the variables that are most significant to the settlement. Then, from the three perspectives of permutation importance, Sobol's variance and Shapley additive explanations analysis, the influence of input features to output are quantified and the features are organically combined to construct the subsets for a random forest (RF) model. The monitoring data from a tunnel construction case across the Yellow River is used to evaluate this model. The variable importance measures (VIMs) based RF model with less variables than the original RF model shows a similar performance. Compared to the principal component analysis (PCA) based RF model, VIMs-based RF model shows a better performance while retaining the feature's physical information, which is critical for future studies that continue to explore the explicit expression of settlement prediction.