Sparsity in optimal randomized classification trees

•We propose a sparse classification tree approach, solved by continuous optimization.•The number of predictor variables used is controlled (local and global sparsity).•Theoretical results on the range of the sparsity parameters are provided.•Empirically, both sparsities can improve while preserving classification performance.•Accuracy results are competitive against benchmark tree-based methods.