Data Science Asked by ricecooker on April 9, 2021
I have a large data set with many features (70). By doing preprocessing (removing features with too many missing values and those that are not correlated with the binary target variable) I have arrived at 15 features. I am now using a decision tree to perform classification with respect to these 15 features and the binary target variable so I can obtain feature importance. Then, I would choose features with high importance to use as an input for my clustering algorithm. Does using feature importance in this context make any sense?
It might make sense, but it depends what you're trying to do:
Correct answer by Erwan on April 9, 2021
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