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Machine learning via H2O
Stata's integration of H2O machine learning provides a powerful, scalable, and user-friendly framework for applying modern machine learning techniques. Interact with an H2O cluster seamlessly within Stata to train and evaluate predictive models efficiently. Obtain predictions and use Shapley additive values, partial dependence plots, and more to explain those predictions. You can use the h2oml commands with familiar Stata syntax or let the point-and-click Control Panel interface guide you through your end-to-end data-analysis process. See more features here.

Lasso
With Stata's lasso and elastic-net features, you can perform model selection and prediction for your continuous, binary, and count outcomes. Want to estimate effects and test coefficients? With lasso inferential methods, you can make inferences for variables of interest while lassos select control variables for you. You can even account for endogenous covariates. See more features here.

Bayesian variable selection
The bayesselect command provides a flexible Bayesian approach to variable selection by using specially designed priors for coefficients, such as global–local shrinkage and spike-and-slab priors. It accounts for model uncertainty when estimating model parameters and allows you to perform Bayesian inference for regression coefficients. bayesselect is fully integrated into Stata's Bayesian suite and works seamlessly with all Bayesian postestimation routines, such as those for Bayesian predictions and diagnostics.