What is your take on feature selection in machine learning? Why is it so important to consider?

Question

Anonymous · Accepted Answer

Feature selection is important because bad or random features can be damaging to
an ML model and it might actually fit on the noise (overfit). Using only the
relevant feature should improve the efficiency and accuracy.

Anonymous · Answer

feature selection is an affective way to reduce the complexity of the model,
when there is not enough data or we have missing data for some features,
moreover, it could help to reduce the bias in the model for some features like
gender or race.  There are two ways for doing feature selection: automatic
feature selection using techniques like L1 regularization, and the other way is
to manually analyze the affect of each feature on the prediction performance.
The later is very effective when using traditional feature engineering, however,
with representation learning and high dimensional features, it can be more
expensive. 
The manual analyziz involves techniques like leave one out and see the affect of
the feature on the performance, this could be very expensive for Deep neural
networks.

What is your take on feature selection in machine learning? Why is it so important to consider?

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