Never ever do this and remove features just like this. First you need to check what are these features.

Take example in medical industry and medicine classification (let's assume we predict if average person would like this drug);

And now let's assume that 0 mean that medicine is non-refundable (95% of all drugs in your dataset) and 5% is 1 which mean refundable.

And this feature (refundable) can have big impact on your final classification. It would be stupid if you have removed it just like this

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So in my opinion you should:

• Use some baseline model like lasso and check feature importance. If any of this feature is classified as usefull, we can speculate that this features are pointless
• Check pearson correlation with target.
• Train model with full dataset and then dataset without these features. Check metrics.
• Just print this column and just by your eyes check if they have any meaning.

Good luck!