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That may be a lot of new binary variables. Your resulting dataset will be sparse (numerous zeros). Feature choice prior could possibly be a good suggestion, also try out just after.
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I'm working with linear SVC and wish to try and do grid search for locating hyperparameter C worth. Soon after receiving value of C, fir the design on educate facts after which you can examination on take a look at info.
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Possessing irrelevant features inside your information can reduce the precision of numerous versions, Particularly linear algorithms like linear and logistic regression.
user211037 3923611 2 The usage of "from numpy import *" is a bad exercise. It pollutes the worldwide namespace. "import numpy as np" is best. In case you have distinct features you utilize a lot, therefore you are Sick and tired of writing np.
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