There isn't any “finest†view. My assistance is to test setting up types from different sights of the data and see which ends up in improved skill. Even take into account developing an ensemble of designs developed from various sights of the information together.
In any case, the characteristics reduction technics which embedded in certain algos (similar to the weights optimization with gradient descent) provide some response to the correlations difficulty.
The final results of every of such tactics correlates with the result of Some others?, I indicate, makes sense to utilize multiple to validate the attribute range?.
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-For the construction of the model I was intending to use MLP NN, utilizing a gridsearch to enhance the parameters.
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I haven’t browse all of the feedback, so I don’t know if this was mentioned by another person. I stumbled across this:
how to proceed with correlated features? really should we change them to some thing new? a combination it's possible? how does it impact our modeling and prediction? appreciated for those who direct me into some sources to review and come across it out.