September 2018
Beginner to intermediate
154 pages
3h 51m
English
Content preview from Mastering Predictive Analytics with scikit-learn and TensorFlow
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Removing dummy features with low variance
The first technique of feature selection that we will learn about is removing dummy features with low variance. The only transformation that we have been applying so far to our features is to transform the categorical features using the encoding technique. If we take one categorical feature and use this encoding technique, we get a set of dummy features, which are to be examined to see whether they have variability or not. So, features with a very low variance are likely to have little impact on prediction. Now, why is that? Imagine that you have a dataset where you have a gender feature and that 98% of the observations correspond to just the female gender. This feature won't have any impact on prediction ...
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