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I have a data set which contains site usage behavior of users over a period of six months. It contains data about:

  • Number of pages viewed
  • Number of unique cookies associated with each user
  • Different number of OS, Browsers used
  • Different number of cities visited

Everything over here is collected on a six month time frame. I have used this data to train a model to predict a target variable 'y'. Everything is numeric in format.

Now I know since its a six month data, and the model is built upon this 6 months of data, I can use this to predict on the next six month data to get target variable y.

My question is that if instead of using it to predict on six month time frame, I use the model to predict on monthly time frame, will it give me incorrect results?

My logic tells me yes, as for example, I used tree method such as Decision tree and Random forest, these algorithms kind of makes thresholds to give output "0/1". Now the variables I mentioned above such as number of cookies associated, OS, Browser etc would have different values if we look at it from one month stand point and if we look at it from 6 months standpoint. For example, number of unique cookies associated with a user would be less if seen over a month where as it will be more if seen from 6 months standpoint.

But I am confused as to if the model will automatically adjust these values while running on monthly data or not. Request you to help me understand the if I am thinking this right or wrong. Also please provide logical explanation if possible.

Thanks.

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