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How does scikit learn select whether a term is important or not from the coefficients?

How does scikit learn select whether a term is important or not from the coefficients?
I’m new to scikit learn and I’m working through a tutorial on SVM’s.
I have the SVM in the tutorial given the coefficients from sklearn.svm.SVC.
In the section that was reading the coefficients and training the model, it says these coefficients are regularized.
My question, how does the program know which terms are important and which terms are not important?
In other words, how does the program discriminate whether a term is important or not?
For example, the coefficients for the terms 0,1,2 are [0.3, 0.7, 0.4]
Does the program learn that 2 is an important term or is that just random luck?

A:

The given values are the parameters of a linear SVM classifier, so you can refer to this discussion to answer your question about
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danw
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