How many training and testing data should i use? [closed] - pandas

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I'm building my arima model and I have a quarterly data. The total dataset is 104 (1996-2021), now how many train data and test data should I use? Inorder to have an accurate forecast data. Thanks

For ARIMA models, the rule of thumb is that you should have at least 50 but preferably more than 100 observations.
You can split the whole datasets at a ratio of 80 to 20.
80% for training and 20% for testing.

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For the model in the pandas dataset, is it okay to use the target column when adding a new feature? [closed]

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For the model in the pandas dataset, is it okay to use the target column when adding a new feature?
How does it affect the model ?
No, the general rule is you should not utilize the data which will not be available in testing or real-application. It will probably result in overfitting.

How can I visualize network architectures effectively? [closed]

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Is there some sort of software that can do so? Specifically, I would like to visualize Resnet18. Is there no other way other than to just draw it myself? Here is an example of what I want to see:
Sample Architecture Visualization
You can use this one : http://alexlenail.me/NN-SVG/LeNet.html . It lets you visualize neural networks by letting you modify several parameters and finally lets you export the architectures as SVG files. You can also choose between 3 visualization styles, namely FCNN, LeNet & AlexNet.

Is it possible to increase the TPS on a a paper server to make red stone computation faster? [closed]

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I normally run a paper server on my computer for single player and multiplayer but I'm wondering if its possible to increase the TPS to that things like repeaters have less delay.

Normal distribution of input data [closed]

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Should I normalize input data to normal distribution before fit it into RNN? If yes, why? At the moment almost all the columns are right shifted, so it's not a normal distribution at all.
You do not necessarily need to transform the inputs to a normal distribution, but you might want to preprocess them so that the majority of each of their values is between 0 and 1. Otherwise, when using sigmoid functions for internal nodes, you may cause saturation. If your inputs are each U(0, 1000), for example, then there's no need to transform to normal distributions, but rather to just scale by 0.001.

Emailing about Code freeze [closed]

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I need to notify my team about the approaching code freeze, So what all details should be mentioned in the mail? I want to prepare a standard template.
Thanks in advance
I usually mention the following items:
Code freeze date (date/time of last commit).
Name of the branch that'll be used for the freeze.
Grace period length for critical in-progress issue.
What release/build is this for.
Feature set of such release.