I'm fairly new to machine learning and I am using the Linear SVM classifier to classify some text data and I was wondering what exactly does the fit_intercept parameter does and what would be a good reason to set it either True or False.
According to the scikit learn documentations what it does is:
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be already centered).
I know the intercept is the point where a function intercepts the y axis but I'm not sure how fitting it helps and why it could be needed. Would the hyperplane of the SVM be better positioned? Does it make the training faster? what does centering the data helps for and what does that exactly mean? Does it make sense to use it with a classification task or is it only useful with regression?