Using various techniques, you can determine relevant and useful features for a specific machine-learning problem. You can start by conducting exploratory data analysis, using domain knowledge, and leveraging statistical measures such as correlation and mutual information. You can also use feature importance methods like decision trees or regularization techniques to assess the impact of features on the model's performance. You can use feature selection algorithms, like recursive feature elimination, to identify the most relevant features based on their contribution to the model's accuracy.