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I am new to ML and am executing a project in Python, at some time it's to acknowledge correlated features , I wonder what will be the subsequent phase?
LSTMs master the construction romance in input sequences so nicely that they can create new plausible sequences.
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I did take a look at both circumstance but results are various, exemple (very first case column A and B are crucial but 2nd scenario column C and D are crucial)
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or you should counsel me some other approach for this sort of dataset (ISCX -2012) where concentrate on course is categorical and the original source all other attributes are constant.
No, you will need to choose the amount of features. I'd personally advocate utilizing a sensitivity Assessment and check out a amount of various characteristics and find out which results in the best executing model.
Many thanks for yourself great publish, I have an issue in feature reduction applying Principal Part Investigation (PCA), ISOMAP or every other Dimensionality Reduction strategy how will we ensure about the number of characteristics/dimensions is best for our classification algorithm in case of numerical information.