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I’m addressing a project the place I have to use unique estimators (regression products). can it be accurate use RFECV with these types? or could it be plenty of to use only one of these? After I've picked the best functions, could I use them for each regression model?

That may be a whole lot of latest binary variables. Your resulting dataset are going to be sparse (numerous zeros). Characteristic selection prior may very well be a good idea, also try right after.

The R & BioConductor handbook delivers a general introduction on the use of the R atmosphere and its primary command syntax.

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I've a dataset which incorporates equally categorical and numerical capabilities. Should I do attribute choice in advance of a single-warm encoding of categorical attributes or following that ?

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Really I had been unable to be aware of the output of chi^two for element assortment. The trouble has long been solved now.

Is there a means similar to a rule of thumb or an algorithm to instantly come to a decision the “best of the greatest”? Say, I use n-grams; if I use trigrams with a one thousand instance data set, the quantity of features explodes. How am i able to set SelectKBest to an “x” amount mechanically according to the best? Thanks.

In combination with which the Elo Rating process (used in chess) is one of my capabilities. Using this characteristic only my accuracy is ~sixty five%.

Most likely, there isn't any a single very best list of features for your personal dilemma. There are lots of with various talent/capability. Discover a established or ensemble of sets that actually works very best for your needs.

With this section we shift from sequential code that basically runs one particular line of code soon after An additional to conditional code the place some methods are skipped. It's really a very simple principle - but it is how computer software tends to make "choices".

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