Use ensemble techniques like bagging to train multiple models on different subsets of the dataset, ignoring the irrelevant features.

Use ensemble techniques like boosting to train models sequentially, focusing on the errors made by previous models and selecting the most relevant features.

Use ensemble techniques like stacking to combine the predictions of multiple models, each trained on a different subset of features, to reduce the impact of irrelevant features.

All of the above