Entropy an end to the data love affair

Matt Gershoff
CEO Conductrics
www.conductrics.com twitter:@mgershoffMatt Gershoff www.conductrics.com Twitter:@mgershoff
Entropy:
An End to the Data
Love Affair

Who am I?
Co-founder of Conductrics software. We help
companies solve their last mile problem for
analytics.
Why me?
Conductrics blends ideas from AB Testing, RL,
Statistics, and Information Theory to generate
human interpretable machine learning models
that target customers with the experiences they
care about. www.conductrics.com
Conductrics Confidential 2

We All
Love Data!
Matt Gershoff www.conductrics.com
Twitter:@mgershoff

But should
we love
data?
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Losing Ticket
Not Surprising
Not Interesting
Not Informative
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Winning Ticket
Surprising
Interesting
Informative
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If an event is Predictable it is
Not Informative
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Key Idea

If an event is Not Predictable
it is Informative
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Key Idea

Information=?
Matt Gershoff www.conductrics.com Twitter:@mgershoff
But What is Information Really?

Data IS NOT
Information
Key Idea

Data Is Only Potential
Information
Key Idea

Source: https://www.thedailybeast.com/claude-shannon-the-juggling-poet-who-gave-us-the-
information-age Photo: Alfred Eisenstaedt/The LIFE Picture Collection/Getty Images
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Studied at MIT
In 1948 while at Bell Labs published
A Mathematical Theory of Communication
Claude Shannon
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What is
Information
if not Data?

Information in data is
equal to the smallest*
possible lossless
encoding
*smallest on average

The More Predictable
The More Compression
The Less Information
Claude Shannon
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Shannon Entropy is
Measure of
Unpredictability in Data
Claude Shannon
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Entropy is the
Minimum number* of
Bits to fully encode data
* - average number Matt Gershoff www.conductrics.com
Twitter:@mgershoff

* - average number
…or equivalently
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Entropy is the
Minimum number* of
QUESTIONS to identify all
possible values in the data
* - average number Matt Gershoff www.conductrics.com
Twitter:@mgershoff

*smallest on average

I see you are confused. To
help with intuition, lets play a
game of 20 Questions. What
letter am I thinking of: A; B;
C; or D?

No
No Yes
Yes
No Yes
Is it ‘C’ or ‘D’?
More Than 1? More Than 3?
1st Question
2nd Question
1 2 3 4
First Approach:
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Questions to Bits to Information

No
No Yes
Yes
No Yes
More Than 3?
1st Question
2nd Question
A B 3 4
Is it ‘B’?
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No
No Yes
Yes
No Yes
1st Question
2nd Question
A B C D
Is it ‘B’? Is it ‘D’?
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We can ALWAYS Pick the Letter
after 2 Questions
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What if we swap 0|1 for No|Yes?
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Each Y|N Question = 1 Bit
A 00
B 01
C 10
D 11
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Questions Bits

We can identify each Letter
with 2 bits
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A 0 0
B 0 1
C 1 0
D 1 1
Bit1 Bit2

So the number of bits in this
case is equal to the number of
Y|N Questions
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Mind Blown

A = 50%
B = 25%
C = 12.5%
D = 12.5%
It gets better. What if I think of the
Letters based on this prior
distribution?
*From http://www.inference.org.uk/mackay/itprnn/
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Can we now do better, on average,
than 2 Questions, or 2 Bits?
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I don’t understand. Maybe you
show me how this works.
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Is it A?
Y N
A
1st Question
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Is it A?
Y N
A Is it B?
Y N
B
1st Question
2nd Question
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Is it A?
Y N
A Is it B?
Y N
B Is it C?
Y N
C
1st Question
2nd Question
3rd Question
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Is it A?
Y N
A Is it B?
Y N
B Is it C?
Y N
C D
1st Question
2nd Question
3rd Question
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Is it A?
Y N
A Is it B?
Y N
B Is it C?
Y N
C D
1st Question
2nd Question
3rd Question
Data Encoding
A=1
B=01
C=001
D=000
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In general, the average number of
questions needed is:
෍
1
𝑘
𝑃 𝐿𝑒𝑡𝑡𝑒𝑟i ⋅ Num Questions𝑖
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Average Number of Questions Needed?
0.5*1 + 0.25*2 + 0.125*3 + 0.125*3 = 1.75
A B C D
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෍
1
4
𝑃 𝐿𝑒𝑡𝑡𝑒𝑟i ⋅ Num Questions𝑖

Can we on average do better than 2
Questions or bits?
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YES!!!
Can we on average do better than 2
Questions or bits?
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How much better depends on the prior
probabilities
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The more predictable the data the fewer
questions or bits needed
Maximum = 2 Our Case = 1.75 Minimum= 0
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𝐌𝐚𝐭𝐡 𝐓𝐢𝐦𝐞
Twitter:@mgershoff

Information
Entropy

From Data to Information
Information Content of an individual event,
or value, like a letter

− log2 𝑃 𝑥𝑖
Information Content of an individual event,
or value, like a letter

Information Content of an Event (𝑥i)
Prob of the Event

Information Content of an Event (𝑥i)
Information is measured in Bits!

Entropy: H(x)
෍
1
𝑘
−𝑃 𝑥i ⋅ log2 𝑃 𝑥𝑖
1) Calculate the Information in each event
2) Take Weighted Average of Information

෍
1
𝑘
Entropy: H(x)

Entropy: H(x)
෍
1
𝑘
Hmm… looks suspiciously similar
to the ave number of questions
calculation

Calculate the Entropy of our Letters
A = 50%
B = 25%
C = 12.5%
D = 12.5%
Entropy: H(x)
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First Calculate the log2 𝑃 𝑥𝑖 :
A = Log(.5) = -1
B = Log(.25) = -2
C = Log(.125) = -3
D = Log(.125) = -3
Entropy: H(x)
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Entropy: H(x)
෍
1
𝑘
-1*(0.5*-1 + 0.25*-2 + 0.125*-3 + 0.125*-3) =
1.75
A B C D
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Entropy: H(x)
෍
1
𝑘
-1*(0.5*-1 + 0.25*-2 + 0.125*-3 + 0.125*-3) =
1.75
But that’s exactly
the same result!Matt Gershoff www.conductrics.com
Twitter:@mgershoff

Entropy = Number of Bits = Number Questions
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What can I do with it?
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Information Gain
Turn Prediction Targeting in to
Problem of Reducing Entropy
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Information Gain
Key Idea Behind Decision Trees
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Example: Conductrics Predictive Segments

Information Gain
𝐼 𝑥; 𝑦 = 𝐻 𝑥 − 𝐻(𝑥|𝑦)
Entropy of
Target Variable
Conditional Entropy
of Target given
Feature Y
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Example Data
Convert Windows Mobile
N N N
N N N
N N N
N N N
N N Y
N Y Y
Y N Y
Y Y N
Y Y N
Y Y N
Y Y Y
Y Y Y
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Summary Statistics
Variable Coverage
Conversion
Rate Entropy
Windows Y 50% 83% 0.65
Windows N 50% 17% 0.65
Mobile Y 42% 60% 0.44
Mobile N 58% 43% 0.99
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Information Gain
1-.5*.65+.5*.65 = 0.35
Gain for Windows*
1-.42*.44+.58*.99 = 0.24
Gain for Mobile
Yes No
Yes No
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Note: In this case Windows Yes and No are symmetrical, so results for each are the
same, but this need not be true. You can see this in Mobile – the two are not the same.

Information Gain
Build Tree
Overall Conversion Rate
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Information Gain
Split By Windows
Overall Conversion Rate
WindowsNO Yes
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Decision Trees
Repeat For each Leaf Node
Stop when:
• Node Size is below threshold
• Information gain is below threshold
• Tree Depth reaches defined limit
These are all parameters YOU set to control
overfitting Matt Gershoff www.conductrics.com
Twitter:@mgershoff

Twitter:@mgershoff
Don’t Love
Data!

Love
Information
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෍
1
𝑘
I
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Thank You!!!
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Psst …much more than Decision
Trees
Information Gain is also Kullback-Leibler Divergence
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Resources
Information Theory
David Mackay’s Course
Information Theory, Pattern Recognition and Neural Networks
http://www.inference.org.uk/mackay/itprnn/
https://www.youtube.com/channel/UCfoScwn69ekXXWNTN0
CLGXA
Mackay’s Free Online Text Book
http://www.inference.org.uk/mackay/itila/
Decision Tree ID3 Algo
https://en.wikipedia.org/wiki/ID3_algorithm
Twitter:@mgershoff

Entropy an end to the data love affair

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Entropy an end to the data love affair