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Did you accidently contain The category output variable in the data when executing the PCA? It should be excluded.

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Getting irrelevant features with your details can reduce the accuracy of numerous designs, Primarily linear algorithms like linear and logistic regression.

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Many thanks with the publish, but I feel heading with Random Forests straight absent will never work In case you have correlated attributes.

It employs the model accuracy to establish which attributes (and mixture of characteristics) contribute quite possibly the most to predicting the target attribute.

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I had been wanting to know if I could Establish/coach another model (say SVM with RBF kernel) using the attributes from SVM-RFE (whereby the kernel utilised is often a linear kernel).

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