Giving higher priority to certain inputs in SKLearn Random Forest Here is the sample data I have:

Tag 1(Val: X), Tag 2(Val: Y), Tag 3(Val: Z), Label (Val: P)

Tag 1(Val: A), Tag 2(Val: B), Tag 3(Val: C), Label (Val: Q)

Tag 1(Val: D), Tag 2(Val: E), Tag 3(Val: F), Label (Val: R)

Tag 1(Val: G), Tag 2(Val: H), Tag 3(Val: I), Label (Val: S)

I am using Scikit Learn Random Forest Classifier.
I want the Classifier to give higher priority to certain inputs.
For Example: 100x priority to Tag 1: 10x priority to Tag 2: 1x priority to Tag 3.

Does Scikit-Learn have any parameters where I can specify this?

[Since the purpose of the Random Forest is to learn the priority of the inputs, is my question nonsensical?]

Appreciate your insight and support.
You've kind of answered your own question there!

One of the strengths of machine learning with algorithms like Random Forests is that they are able to determine the feature (input) importance themselves. My advice would not be to weight them for now (not sure you actually can with SKLearn's RF) and let the model determine it itself.

What you can do is give additional weight to the rows (observations) of your data set, or give additional weight to different classes of the label (for example, if there aren't many 'P's in your label but they're important, you can bump up their weight relative to the other classes).