[TriPython] Prediction Model. Data Visualization.
Art
artem.nesterenko at gmail.com
Wed Oct 11 13:19:25 EDT 2017
Francois,
Thank you for your response! I like the idea of building a heatmap or using
yellow brick rather than going with the bar graph.
Will see if I can make it happen...
I'd like to say a big thank you to everyone for your suggestions and links!
Now I have plenty of materials to work with.
It was a good idea to email this group:)
Best,
Art.
Art Nestsiarenka
email: artem.nesterenko at gmail.com
Cell: (919) 455-5055
On Wed, Oct 11, 2017 at 12:18 PM, Francois Dion <francois.dion at gmail.com>
wrote:
> Art (and list members interested in visualization),
>
> As Dave mentioned, donut charts work best for progress to goal. ie. a
> percentage. Like a dashboard guage. Or something where the 50% mark is
> important, say a win/loss indicator of the Carolina Hurricanes against
> visitor. Similarly, the ancestor of donut chart, the pie chart is best
> suited for parts of a whole when you have 2 or 3 elements at most.
> Beyond
> that, it is almost impossible to figure out the percentages and relative
> importance. Bar charts do much better when there are more than 2 or 3
> values.
>
> A confusion matrix, in the simplest binary case, bins 4 possible outcome
> of a classifier. True positive (you are part of the class and I said
> so),
> false positive (you are not part of the class but I said you were), true
> negative (you are not part of the class and I said so) and false
> negative
> (you are part of the class but I said you were not). The expectation of
> representation of a confusion matrix, is unsurprisingly, as a matrix.
> The
> standard way to represent this is in a table format, a matrix (of actual
> against predicted), hence the name. This has been the case since at
> least
> the 1950s (without doing an exhaustive search, just from memory). For
> example, I just pulled Mike James' "Classification Algorithms" from
> 1985,
> page 83, and there it is. He also sums each row and column.
>
> But, sure, the plain text table is a bit drab if you are looking for
> maximum impact. So, that's where I was suggesting a heatmap. Or you can
> use the python package yellow brick.
>
> Here's an example using seaborn's heatmap (and making sure I label the
> axis, else it is useless). I used cmap="Greens":
>
> [1]https://datasciencefrancois.tumblr.com/post/166291770900/
> confusion-matrix-with-a-single-color-sequential
>
> I've had no problem using this with technical and non technical
> audiences.
> Shown CMs like the above (and a variety of other graphical and
> semigraphical displays) to business folks who then proceeded to green
> light further phases of fairly large data science projects. Once they've
> seen one and got it you never have to explain it again. Without the
> heatmap colors, it was super challenging to have people "get it".
> Also, you might be interested in this list of books on visualization
> (from
> my "ex-libris" series on linkedin):
>
> [2]https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-
> visualization-francois-dion/
>
> In particular, Stephen Few's "Show Me the Numbers : Designing Tables and
> Graphs to Enlighten" should definitely be on everyone's reading list,
> along with Cairo's "The Functional Art", will get you started, if you
> can't commit to reading 1 viz book per week for the next 2 years :)
>
> Thanks,
> Francois
> On Wed, Oct 11, 2017 at 8:52 AM, Art <[3]artem.nesterenko at gmail.com>
> wrote:
>
> ** **Donut graph:
> ** **[1][4]https://imgur.com/a/C7r8x
> ** **You should be able to see it now.
> ** **Art Nestsiarenka
>
> --
> [5]about.me/francois.dion - [6]www.pyptug.org - [7]
> www.3DFutureTech.info -
> [8]@f_dion
>
> References
>
> Visible links
> 1. https://datasciencefrancois.tumblr.com/post/166291770900/
> confusion-matrix-with-a-single-color-sequential
> 2. https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-
> visualization-francois-dion/
> 3. mailto:artem.nesterenko at gmail.com
> 4. https://imgur.com/a/C7r8x
> 5. http://about.me/francois.dion
> 6. http://www.pyptug.org/
> 7. http://www.3dfuturetech.info/
> 8. http://twitter.com/f_dion
>
> _______________________________________________
> TriZPUG mailing list
> TriZPUG at python.org
> https://mail.python.org/mailman/listinfo/trizpug
> http://tripython.org is the Triangle Python Users Group
>
>
-------------- next part --------------
Francois,
Thank you for your response! I like the idea of building a heatmap or
using yellow brick rather than going with the bar graph.
Will see if I can make it happen...
I'd like to say a big thank you to everyone for your suggestions and
links! Now I have plenty of materials to work with.****
It was a good idea to email this group:)
Best,
Art.
Art Nestsiarenka
email: [1]artem.nesterenko at gmail.com
Cell: (919) 455-5055
On Wed, Oct 11, 2017 at 12:18 PM, Francois Dion
<[2]francois.dion at gmail.com> wrote:
** **Art (and list members interested in visualization),
** **As Dave mentioned, donut charts work best for progress to goal. ie.
a
** **percentage. Like a dashboard guage. Or something where the 50% mark
is
** **important, say a win/loss indicator of the Carolina Hurricanes
against
** **visitor. Similarly, the ancestor of donut chart, the pie chart is
best
** **suited for parts of a whole when you have 2 or 3 elements at most.
Beyond
** **that, it is almost impossible to figure out the percentages and
relative
** **importance. Bar charts do much better when there are more than 2 or
3
** **values.
** **A confusion matrix, in the simplest binary case, bins 4 possible
outcome
** **of a classifier. True positive (you are part of the class and I
said so),
** **false positive (you are not part of the class but I said you were),
true
** **negative (you are not part of the class and I said so) and false
negative
** **(you are part of the class but I said you were not). The
expectation of
** **representation of a confusion matrix, is unsurprisingly, as a
matrix. The
** **standard way to represent this is in a table format, a matrix (of
actual
** **against predicted), hence the name. This has been the case since at
least
** **the 1950s (without doing an exhaustive search, just from memory).
For
** **example, I just pulled Mike James' "Classification Algorithms" from
1985,
** **page 83, and there it is. He also sums each row and column.
** **But, sure, the plain text table is a bit drab if you are looking
for
** **maximum impact. So, that's where I was suggesting a heatmap. Or you
can
** **use the python package yellow brick.
** **Here's an example using seaborn's heatmap (and making sure I label
the
** **axis, else it is useless). I used cmap="Greens":
**
**[1][3]https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential
** **I've had no problem using this with technical and non technical
audiences.
** **Shown CMs like the above (and a variety of other graphical and
** **semigraphical displays) to business folks who then proceeded to
green
** **light further phases of fairly large data science projects. Once
they've
** **seen one and got it you never have to explain it again. Without the
** **heatmap colors, it was super challenging to have people "get it".
** **Also, you might be interested in this list of books on
visualization (from
** **my "ex-libris" series on linkedin):
**
**[2][4]https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/
** **In particular, Stephen Few's "Show Me the Numbers : Designing
Tables and
** **Graphs to Enlighten" should definitely be on everyone's reading
list,
** **along with Cairo's "The Functional Art", will get you started, if
you
** **can't commit to reading 1 viz book per week for the next 2 years :)
** **Thanks,
** **Francois
** **On Wed, Oct 11, 2017 at 8:52 AM, Art
<[3][5]artem.nesterenko at gmail.com>
** **wrote:
** ** **** **Donut graph:
** ** **** **[1][4][6]https://imgur.com/a/C7r8x
** ** **** **You should be able to see it now.
** ** **** **Art Nestsiarenka
** **--
** **[5][7]about.me/francois.dion - [6][8]www.pyptug.org -
[7][9]www.3DFutureTech.info -
** **[8]@f_dion
References
** **Visible links
** **1.
[10]https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential
** **2.
[11]https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/
** **3. mailto:[12]artem.nesterenko at gmail.com
** **4. [13]https://imgur.com/a/C7r8x
** **5. [14]http://about.me/francois.dion
** **6. [15]http://www.pyptug.org/
** **7. [16]http://www.3dfuturetech.info/
** **8. [17]http://twitter.com/f_dion
_______________________________________________
TriZPUG mailing list
[18]TriZPUG at python.org
[19]https://mail.python.org/mailman/listinfo/trizpug
[20]http://tripython.org is the Triangle Python Users Group
References
Visible links
1. mailto:artem.nesterenko at gmail.com
2. mailto:francois.dion at gmail.com
3. https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential
4. https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/
5. mailto:artem.nesterenko at gmail.com
6. https://imgur.com/a/C7r8x
7. http://about.me/francois.dion
8. http://www.pyptug.org/
9. http://www.3dfuturetech.info/
10. https://datasciencefrancois.tumblr.com/post/166291770900/confusion-matrix-with-a-single-color-sequential
11. https://www.linkedin.com/pulse/ex-libris-data-scientist-part-v-visualization-francois-dion/
12. mailto:artem.nesterenko at gmail.com
13. https://imgur.com/a/C7r8x
14. http://about.me/francois.dion
15. http://www.pyptug.org/
16. http://www.3dfuturetech.info/
17. http://twitter.com/f_dion
18. mailto:TriZPUG at python.org
19. https://mail.python.org/mailman/listinfo/trizpug
20. http://tripython.org/
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