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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# current API updated to use parameters instead" | |
] | |
}, | |
{ |
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Performing Scenario Discovery in Python\n", | |
"\n", | |
"The purpose of this blog post is to demonstrate how one can do scenario discovery in python. This blogpost will use the exploratory modeling workbench available on github. I will demonstrate how we can perform both PRIM in an interactive way, as well as briefly show how to use CART, which is also available in the exploratory modeling workbench. There is ample literature on both CART and PRIM and their relative merits for use in scenario discovery. So I won't be discussing that here in any detail.\n", | |
"\n", |
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