remove V.A.C ban?

The only way to get your vac removed is if you make an unban appeal or contact steam support - Otherwise you're stuck with that VAC Ban, I suggest making a new account.

VAC bans are permanent, non-negotiable, and cannot be removed by Steam Support

Nope you cant get your vac removed too bad for you

Vac bans are only removed if they believe there was some sort of error with the system. This won't work though because it would usually happen to more than one person Any other excuse you can probably come up with won't work.

I'm sad to say it won't work, but atleast you can try to apply to the steam support.

Nope. This is impossible. The vac ban is permantly

you cannot anymore get unbanned from VAC.
even if you contact steam support and tell them your story it wont help
VAC IS VAC you cant get un VAC

Getting unbanned from VAC is literally impossible. I only heared about 1 - 2 people that got unbanned. But they didnt cheat. They just had stuff like Cheat Engine open when playing. You are better of stopping to cheat, buy a better hook, paste your own hook and remove VHT or buy a new CSGO or a new CSGO key.

thanks a lot it works thanks a lot it works thanks a lot it works thanks a lot it works thanks a lot it works

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OK
So, we've created a general package for scikit-learn decision tree visualization and model interpretation, which we'll be using heavily in an upcoming machine learning book (written with Jeremy Howard). Here's a sample visualization for a tiny decision tree (click to enlarge)

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xample, we couldn't find a library that visualizes how decision nodes split up the feature space. It is also uncommon for libraries to support visualizing a specific feature vector as it weaves down through a tree's decision nodes; we could only find one image showing this.

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decision tree is a machine learning model based upon binary trees (trees with at most a left and right child). A decision tree learns the relationship between observations in a training set, represented as feature vectors x and target values y, by examining and condensing training data into a binary tree of interior nodes and leaf nodes