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It uses the product accuracy to discover which characteristics (and blend of characteristics) contribute probably the most to predicting the goal attribute.
I've a dataset which has both categorical and numerical features. Need to I do feature choice in advance of just one-very hot encoding of categorical options or after that ?
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In sci-package master the default worth for bootstrap sample is fake. Doesn’t this contradict to locate the attribute great importance? e.g it could build the tree on just one feature and Therefore the worth can be higher but will not stand for The complete dataset.
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or make sure you advise me Several other approach for this type of dataset (ISCX -2012) wherein focus on class is categorical and all other attributes are steady.
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what to do with correlated functions? need to we change them to a thing new? a mixture it's possible? How can it have an impact on our modeling and prediction? appreciated for those who direct me into some resources to study and come across it out.