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I'm now in the middle of the semester and trying to understand the background of the algorithms and features. I would like to understand some theory.

If I have a dataset with N samples. each sample has 5 features for example. I have done 3 kinds of classifications algorithms for example : SVM, decision tree and kMeans. In all 3, I got nice results

In a mystery way, a new feature added to the dataset. The value of the features for every sample selected randomly.

I restarted the algorithms on the dataset ( with the new feature)

Are the classification results gonna change from the first results without the new feature? If yes, why are they gonna change and by how much ? In addition, if I do not have the dataset how can I know how to recognize that new feature?

G5W
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1 Answers1

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The results of your classification algorithm are going to either change or stay the same depending on how much information the model gains from the feature. If the feature for instance is random noise then it will have little to no effect on your model, other than slowing it down. If it contains useful information it might be able to increase parameters such as recall and precision. Hope this might help.

joshniemela
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