|
|
|
|
|
|
|
|
|
 |
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
[Subscribe]
Re: DM: Datamining tools ... black boxes...From: Ronny Kohavi Date: Wed, 16 Feb 2000 01:38:58 -0800
Warren Sarle wrote:
> > "Some Data Mining tools are presented like black boxes" simply because
> > they ARE black boxes. It is not possible to make much sense of say, a
> > trained
> > Neural Network or the Memory Based Reasoning algorithm results. You have
> > to believe the underlying math in order to trust their predictions.
>
> That is not what "black box" means. "Black box" means you know what
> is supposed to go into it and you know what is supposed to come out
> of it, but you you don't know how the computation is done inside the
> "box". It has nothing to do with interpretability of results. With
> respect to commercial software, "black box" means that the algorithms
> used inside the software are undocumented.
>
I beg to differ here, Warren; I agree with Sergei.
A black box means that you know the characteristics of the construct/box,
but it's "black" because the internals are unspecified or not understood
by the person looking at the box.
For example, a black box that makes product recommendations at a web site
can have clear input/outputs:
input=shopping basket and prior purchases,
output=top 3 products to recommend
You can study its input/outputs to try and glean insight, but that's
going to be very hard unless you can open that box and find something
you can understand.
If the recommendation engine uses a neural network or
memory-based/nearest-neighbor
approaches, good luck explaining its behavior to a business user (it will
remain a
black box).
If, however, the box makes recommendations based on decision rules or
trees, a business
user might understand what's inside, turning it from black-box to white-box.
-- Ronny
|
Copyright © 1999 Nautilus Systems, Inc. All Rights Reserved.MHonArc 2.2.0