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This commit is contained in:
Emanuel Rodriguez 2022-09-05 01:29:44 -07:00
parent cac5ac9243
commit 721d1d83d0
3 changed files with 524 additions and 47 deletions

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@ -243,12 +243,12 @@ Probability and Likelihood
<span id="cb7-9"><a href="#cb7-9" aria-hidden="true" tabindex="-1"></a> gt<span class="sc">::</span><span class="fu">cols_width</span>(<span class="fu">everything</span>() <span class="sc">~</span> <span class="fu">px</span>(<span class="dv">100</span>))</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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@ -273,7 +273,7 @@ Probability and Likelihood
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@ -309,13 +309,13 @@ Probability and Likelihood
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@ -382,7 +382,7 @@ Probability and Likelihood
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@ -462,7 +462,7 @@ Probability and Likelihood
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@ -476,11 +476,11 @@ Probability and Likelihood
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@ -490,16 +490,16 @@ Probability and Likelihood
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@ -676,6 +676,68 @@ Probability and Likelihood
</div>
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</div>
<p>The table above also shows the likelihoods for the case when an article does not contain exclamation point in the title as well. Its really important to note that these are likelihoods, and its not the case that <span class="math inline">\(L(B|A) + L(B^c|A) = 1\)</span> as a matter of fact this value evaluates to a number less than one. However, since we have that <span class="math inline">\(L(B|A) = .267\)</span> and <span class="math inline">\(L(B^c|A) = .022\)</span> then we have gained additional knowledge in knowing the use of “!” in a title is more compatible with a fake news article than a real one.</p>
<p>Up to this point we can summarize our framework as follows</p>
<table class="table">
<thead>
<tr class="header">
<th>event</th>
<th><span class="math inline">\(B\)</span></th>
<th><span class="math inline">\(B^c\)</span></th>
<th>Total</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>prior</td>
<td>.4</td>
<td>.6</td>
<td>1</td>
</tr>
<tr class="even">
<td>likelihood</td>
<td>.267</td>
<td>.022</td>
<td>.289</td>
</tr>
</tbody>
</table>
<p>Our next goal is come up with normalizing factors in order to build our probability table:</p>
<table class="table">
<thead>
<tr class="header">
<th></th>
<th><span class="math inline">\(B\)</span></th>
<th><span class="math inline">\(B^c\)</span></th>
<th>Total</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td><span class="math inline">\(A\)</span></td>
<td>(1)</td>
<td>(2)</td>
<td></td>
</tr>
<tr class="even">
<td><span class="math inline">\(A^c\)</span></td>
<td>(3)</td>
<td>(4)</td>
<td></td>
</tr>
<tr class="odd">
<td>Total</td>
<td>.4</td>
<td>.6</td>
<td>1</td>
</tr>
</tbody>
</table>
<p>A couple things to note about our table (1) + (2) = .4 and (2) + (4) = .6. (1) + (2) + (3) + (4) = 1.</p>
<ol type="1">
<li><p><span class="math inline">\(P(A \cap B) = P(A|B)P(B)\)</span> we know the likelihood of <span class="math inline">\(L(B|A) = P(A|B)\)</span> and we also know the prior so we insert these to get <span class="math display">\[ P(A \cap B) = P(A|B)P(B) = .267 \times .4 = .1068\]</span></p></li>
<li><p><span class="math inline">\(P(A^c \cap B) = P(A^c|B)P(B)\)</span> in this case we do know the prior <span class="math inline">\(P(B) = .4\)</span>, but we dont directly know the value of <span class="math inline">\(P(A^c|B)\)</span>, however, we note that <span class="math inline">\(P(A|B) + P(A^c|B) = 1\)</span>, therefore we compute <span class="math inline">\(P(A^c|B) = 1 - P(A|B) = 1 - .267 = .733\)</span> <span class="math display">\[ P(A^c \cap B) = P(A^c|B)P(B) = .733 \times .4 = .2932\]</span></p></li>
</ol>
</section>
</main>

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@ -114,4 +114,39 @@ prop_of_excl_within_type |>
The table above also shows the likelihoods for the case
when an article does not contain exclamation point in
the title.
the title as well. It's really important to note that these are likelihoods,
and its not the case that $L(B|A) + L(B^c|A) = 1$ as a matter of fact this
value evaluates to a number less than one. However, since we have that
$L(B|A) = .267$ and $L(B^c|A) = .022$ then we have gained additional
knowledge in knowing the use of "!" in a title is more compatible
with a fake news article than a real one.
Up to this point we can summarize our framework as follows
| event | $B$ | $B^c$ | Total |
|------- |-----|-------|------|
| prior | .4 | .6 | 1 |
| likelihood |.267 | .022 | .289 |
Our next goal is come up with normalizing factors in order to build our
probability table:
| | $B$| $B^c$| Total |
|------|----|------|-------|
|$A$ | (1)| (2) | |
|$A^c$ | (3)| (4) | |
|Total | .4 | .6 | 1 |
A couple things to note about our table (1) + (3) = .4 and (2) + (4) = .6.
(1) + (2) + (3) + (4) = 1.
(1) $P(A \cap B) = P(A|B)P(B)$ we know the likelihood of $L(B|A) = P(A|B)$ and we also
know the prior so we insert these to get
$$ P(A \cap B) = P(A|B)P(B) = .267 \times .4 = .1068$$
(3) $P(A^c \cap B) = P(A^c|B)P(B)$ in this case we do know the prior $P(B) = .4$, but we
don't directly know the value of $P(A^c|B)$, however, we note that $P(A|B) + P(A^c|B) = 1$,
therefore we compute $P(A^c|B) = 1 - P(A|B) = 1 - .267 = .733$
$$ P(A^c \cap B) = P(A^c|B)P(B) = .733 \times .4 = .2932$$
we now can confirm that $.1068 + .2932 = .4$

380
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pdftitle={Chapter 2 Notes},
pdfauthor={Emanuel Rodriguez},
colorlinks=true,
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\title{Chapter 2 Notes}
\author{Emanuel Rodriguez}
\date{}
\begin{document}
\maketitle
\ifdefined\Shaded\renewenvironment{Shaded}{\begin{tcolorbox}[interior hidden, enhanced, breakable, frame hidden, sharp corners, boxrule=0pt, borderline west={3pt}{0pt}{shadecolor}]}{\end{tcolorbox}}\fi
In this chapter we step through an example of ``fake'' vs ``real'' news
to build a framework to determine the probability of real vs fake of a
new news article titled ``The President has a secret!''
\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# libraries}
\FunctionTok{library}\NormalTok{(bayesrules)}
\FunctionTok{library}\NormalTok{(dplyr)}
\FunctionTok{library}\NormalTok{(tidyr)}
\FunctionTok{library}\NormalTok{(gt)}
\FunctionTok{data}\NormalTok{(fake\_news)}
\NormalTok{fake\_news }\OtherTok{\textless{}{-}}\NormalTok{ tibble}\SpecialCharTok{::}\FunctionTok{as\_tibble}\NormalTok{(fake\_news)}
\end{Highlighting}
\end{Shaded}
What is the proportion of news articles that were labeled fake vs real.
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{fake\_news }\SpecialCharTok{|\textgreater{}} \FunctionTok{glimpse}\NormalTok{()}
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
Rows: 150
Columns: 30
$ title <chr> "Clinton's Exploited Haiti Earthquake to Stea~
$ text <chr> "0 SHARES Facebook Twitter\n\nBernard Sansaric~
$ url <chr> "http://freedomdaily.com/former-haitian-senate~
$ authors <chr> NA, NA, "Sierra Marlee", "Jack Shafer,Nolan D"~
$ type <fct> fake, real, fake, real, fake, real, fake, fake~
$ title_words <int> 17, 18, 16, 11, 9, 12, 11, 18, 10, 13, 10, 11,~
$ text_words <int> 219, 509, 494, 268, 479, 220, 184, 500, 677, 4~
$ title_char <int> 110, 95, 96, 60, 54, 66, 86, 104, 66, 81, 59, ~
$ text_char <int> 1444, 3016, 2881, 1674, 2813, 1351, 1128, 3112~
$ title_caps <int> 0, 0, 1, 0, 0, 1, 0, 2, 1, 1, 0, 1, 0, 0, 0, 0~
$ text_caps <int> 1, 1, 3, 3, 0, 0, 0, 12, 12, 1, 2, 5, 1, 1, 6,~
$ title_caps_percent <dbl> 0.000000, 0.000000, 6.250000, 0.000000, 0.0000~
$ text_caps_percent <dbl> 0.4566210, 0.1964637, 0.6072874, 1.1194030, 0.~
$ title_excl <int> 0, 0, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0~
$ text_excl <int> 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 2, 0, 0~
$ title_excl_percent <dbl> 0.0000000, 0.0000000, 2.0833333, 0.0000000, 0.~
$ text_excl_percent <dbl> 0.00000000, 0.00000000, 0.06942034, 0.00000000~
$ title_has_excl <lgl> FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE~
$ anger <dbl> 4.24, 2.28, 1.18, 4.66, 0.82, 1.29, 2.56, 3.47~
$ anticipation <dbl> 2.12, 1.71, 2.16, 1.79, 1.23, 0.43, 2.05, 1.74~
$ disgust <dbl> 2.54, 1.90, 0.98, 1.79, 0.41, 1.72, 2.05, 1.35~
$ fear <dbl> 3.81, 1.90, 1.57, 4.30, 0.82, 0.43, 5.13, 4.25~
$ joy <dbl> 1.27, 1.71, 1.96, 0.36, 1.23, 0.86, 1.54, 1.35~
$ sadness <dbl> 4.66, 1.33, 0.78, 1.79, 0.82, 0.86, 2.05, 1.93~
$ surprise <dbl> 2.12, 1.14, 1.18, 1.79, 0.82, 0.86, 1.03, 1.35~
$ trust <dbl> 2.97, 4.17, 3.73, 2.51, 2.46, 2.16, 5.13, 3.86~
$ negative <dbl> 8.47, 4.74, 3.33, 6.09, 2.66, 3.02, 4.10, 4.63~
$ positive <dbl> 3.81, 4.93, 5.49, 2.15, 4.30, 2.16, 4.10, 4.25~
$ text_syllables <int> 395, 845, 806, 461, 761, 376, 326, 891, 1133, ~
$ text_syllables_per_word <dbl> 1.803653, 1.660118, 1.631579, 1.720149, 1.5887~
\end{verbatim}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{fake\_news }\SpecialCharTok{|\textgreater{}}
\FunctionTok{group\_by}\NormalTok{(type) }\SpecialCharTok{|\textgreater{}}
\FunctionTok{summarise}\NormalTok{(}
\AttributeTok{total =} \FunctionTok{n}\NormalTok{(),}
\AttributeTok{prop =}\NormalTok{ total }\SpecialCharTok{/} \FunctionTok{nrow}\NormalTok{(fake\_news)}
\NormalTok{ ) }
\end{Highlighting}
\end{Shaded}
\begin{verbatim}
# A tibble: 2 x 3
type total prop
<fct> <int> <dbl>
1 fake 60 0.4
2 real 90 0.6
\end{verbatim}
If we let \(B\) be the event that a news article is ``fake'' news, and
\(B^c\) be the event that a news article is ``real'', we can write the
following:
\[P(B) = .4\] \[P(B^c) = .6\]
This is the first ``clue'' or set of data that we have to build into our
framework. Namely, majority of articles are ``real'', therefore we could
simply predict that the new article is ``real''. This updated sense or
reality now becomes our priors.
Getting additional data, and updating our priors, based on additional
data. The new observation we make is the use of exclamation marks ``!''.
We note that the use of ``!'' is more frequent in news articles labeled
as ``fake''. We will want to incorporate this into our framework to
decide whether the new incoming should be labelled as real or fake.
\hypertarget{likelihood}{%
\subsubsection{Likelihood}\label{likelihood}}
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When the event \(B\) is known, then we can evaluate the uncertainy of
events \(A\) and \(A^c\) given \(B\)
\[P(A|B) \text{ vs } P(A^c|B)\]
If on the other hand, we know event \(A\) then we can evaluate the
relative compatability of data \(A\) with \(B\) and \(B^c\) using
likelihood functions
\[L(B|A) \text{ vs } L(B^c|A)\] \[=P(A|B) \text{ vs } P(A|B^c)\]
\end{tcolorbox}
So in our case, we don't know whether this new incoming article is real
or not, but we do know that the title has an exclamation mark. This
means we can evaluate how likely this article is real or not given that
it contains an ``!'' in the title using likelihood functions. We can
formualte this as:
\[L(B|A) \text{ vs } L(B^c|A)\]
And perform the computation in R as follows:
\begin{Shaded}
\begin{Highlighting}[]
\CommentTok{\# if fake, what are the proprotions of ! vs no{-}!}
\NormalTok{prop\_of\_excl\_within\_type }\OtherTok{\textless{}{-}}\NormalTok{ fake\_news }\SpecialCharTok{|\textgreater{}}
\FunctionTok{group\_by}\NormalTok{(type, title\_has\_excl) }\SpecialCharTok{|\textgreater{}}
\FunctionTok{summarise}\NormalTok{(}
\AttributeTok{total =} \FunctionTok{n}\NormalTok{()}
\NormalTok{ ) }\SpecialCharTok{|\textgreater{}}
\FunctionTok{ungroup}\NormalTok{() }\SpecialCharTok{|\textgreater{}}
\FunctionTok{group\_by}\NormalTok{(type) }\SpecialCharTok{|\textgreater{}}
\FunctionTok{summarise}\NormalTok{(}
\AttributeTok{has\_excl =}\NormalTok{ title\_has\_excl,}
\AttributeTok{prop\_within\_type =}\NormalTok{ total }\SpecialCharTok{/} \FunctionTok{sum}\NormalTok{(total)}
\NormalTok{ ) }
\end{Highlighting}
\end{Shaded}
\begin{Shaded}
\begin{Highlighting}[]
\NormalTok{prop\_of\_excl\_within\_type }\SpecialCharTok{|\textgreater{}}
\FunctionTok{pivot\_wider}\NormalTok{(}\AttributeTok{names\_from =} \StringTok{"type"}\NormalTok{, }\AttributeTok{values\_from =}\NormalTok{ prop\_within\_type) }\SpecialCharTok{|\textgreater{}}
\FunctionTok{gt}\NormalTok{() }\SpecialCharTok{|\textgreater{}}
\NormalTok{ gt}\SpecialCharTok{::}\FunctionTok{cols\_label}\NormalTok{(}
\AttributeTok{has\_excl =} \StringTok{"Contains Exclamtion"}\NormalTok{,}
\AttributeTok{fake =} \StringTok{"Fake"}\NormalTok{, }
\AttributeTok{real =} \StringTok{"Real"}\NormalTok{) }\SpecialCharTok{|\textgreater{}}
\NormalTok{ gt}\SpecialCharTok{::}\FunctionTok{fmt\_number}\NormalTok{(}\AttributeTok{columns=}\FunctionTok{c}\NormalTok{(}\StringTok{"fake"}\NormalTok{, }\StringTok{"real"}\NormalTok{), }\AttributeTok{decimals =} \DecValTok{3}\NormalTok{) }\SpecialCharTok{|\textgreater{}}
\NormalTok{ gt}\SpecialCharTok{::}\FunctionTok{cols\_width}\NormalTok{(}\FunctionTok{everything}\NormalTok{() }\SpecialCharTok{\textasciitilde{}} \FunctionTok{px}\NormalTok{(}\DecValTok{100}\NormalTok{))}
\end{Highlighting}
\end{Shaded}
\begin{longtable}{crr}
\toprule
Contains Exclamtion & Fake & Real \\
\midrule
FALSE & $0.733$ & $0.978$ \\
TRUE & $0.267$ & $0.022$ \\
\bottomrule
\end{longtable}
The table above also shows the likelihoods for the case when an article
does not contain exclamation point in the title.
\end{document}