From 622823160914dc623747212ba4e1f7ac943fed15 Mon Sep 17 00:00:00 2001
From: Brandon Rodriguez <brodriguez8774@gmail.com>
Date: Mon, 8 Jun 2020 09:09:48 -0400
Subject: [PATCH] Create assignment pdf report

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 documents/references.md |   3 +
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diff --git a/documents/Latex/a4.tex b/documents/Latex/a4.tex
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+\documentclass[a4paper,12pt]{article}
+
+
+% For latex basics, see http://www.docs.is.ed.ac.uk/skills/documents/3722/3722-2014.pdf.
+% For help on symbols, see https://oeis.org/wiki/List_of_LaTeX_mathematical_symbols.
+% For info on state machines, look up youtube "Neso Academy" channel.
+
+
+% Import Packages.
+% Multi-column package. Allows formatting in columns.
+\usepackage{multicol}
+
+
+% Adjust page margins to not be so excessive.
+\addtolength{\oddsidemargin}{-.2in}
+\addtolength{\evensidemargin}{-.2in}
+\addtolength{\textwidth}{.4in}
+\addtolength{\topmargin}{-.8in}
+\begin{document}
+	\title{CS6530 Assignment 4 Report}
+	\author{Brandon Rodriguez}
+	\date{\today}
+	\maketitle
+
+	\vspace{-3ex}~
+	
+	
+	\section* {Part 1}
+	See "a4.r" file, for descriptions of what I think the provided code is doing.\\
+	\\
+	\\
+	Note that, even after googling, I could not determine what "\textbf{Species\~.,iris}" does, which is a segment of code used in 3 out of 4 of the provided code examples.\\
+	\\
+	Taking an educated guess, I think this is the equivalent of "\textbf{iris[['Species']]}" or "\textbf{iris\$Species}", except that it seems to automatically format the data in some way such that rpart accepts it?\\
+	\\
+	Either way, it seems important for the code to function, and appears to indicate the attribute to use as the classifier label result.
+
+	\pagebreak
+	\section* {Part 2.1 - Balance Dataset}
+	\subsection* {2.1) Decision Tree Results}
+	\textbf{Predictions (Confusion Table)}:\\
+	\begin{tabular}{|cc|ccc|}
+		\hline
+		~ & ~ & Orig & ~ & ~ \\
+		~ & ~ & B & L & R \\
+		\hline
+		Predic & B & 0 & 0 & 0 \\
+		~ & L & 7 & 106 & 6 \\
+		~ & R & 7 & 10 & 89 \\
+		\hline
+	\end{tabular}~\\
+	\\\\
+	\textbf{Stat Summary}:\\
+	Total Accuracy: 0.8667\\
+	\\
+	\begin{tabular}{|c|ccc|}
+		\hline
+		~ & B & L & R \\
+		\hline
+		Balanced Accuracy & 0.5000 & 0.8973 & 0.9030 \\
+		Sensitivity/Recall & 0.0000 & 0.9138 & 0.9368 \\
+		Specificity & 1.0000 & 0.8807 & 0.8692 \\
+		Precision & NaN & 0.8908 & 0.8396 \\
+		\hline
+	\end{tabular}
+	
+	\subsection* {2.1) Forest Results}
+	\textbf{Predictions (Confusion Table)}:\\
+	\begin{tabular}{|cc|ccc|}
+		\hline
+		~ & ~ & Orig & ~ & ~ \\
+		~ & ~ & B & L & R \\
+		\hline
+		Predic & B & 14 & 0 & 0 \\
+		~ & L & 0 & 116 & 0 \\
+		~ & R & 0 & 0 & 95 \\
+		\hline
+	\end{tabular}~\\
+	\\\\
+	\textbf{Stat Summary}:\\
+	Total Accuracy: 1.0\\
+	\\
+	\begin{tabular}{|c|ccc|}
+		\hline
+		~ & B & L & R \\
+		\hline
+		Balanced Accuracy & 1.0 & 1.0 & 1.0 \\
+		Sensitivity/Recall & 1.0 & 1.0 & 1.0 \\
+		Specificity & 1.0 & 1.0 & 1.0 \\
+		Precision & 1.0 & 1.0 & 1.0 \\
+		\hline
+	\end{tabular}
+
+	
+	\section* {Part 2.2 - Nursery Dataset}
+	
+	\subsection* {2.2) Decison Tree Results}
+	\textbf{Predictions (Confusion Table)}:\\
+	\begin{tabular}{|cc|ccccc|}
+		\hline
+		~ & ~ & Orig & ~ & ~ & ~ & ~ \\
+		~ & ~ & not\_rec & priority & rec & spec\_prior & very\_rec \\
+		\hline
+		Predic & not\_rec & 1623 & 0 & 0 & 0 & 0 \\
+		~ & priority & 0 & 1284 & 0 & 147 & 123 \\
+		~ & rec & 0 & 0 & 0 & 0 & 0 \\
+		~ & spec\_prior & 0 & 373 & 0 & 1410 & 0 \\
+		~ & very\_rec & 0 & 0 & 0 & 0 & 0 \\
+		\hline
+	\end{tabular}~\\
+	\\\\
+	\textbf{Stat Summary}:\\
+	Total Accuracy: 0.8704\\
+	\\
+	\begin{tabular}{|c|ccccc|}
+		\hline
+		~ & not\_rec & priority & rec & spec\_prior & very\_rec \\
+		\hline
+		Balanced Accuracy & 1.0000 & 0.8466 & N/A & 0.8980 & 0.5000 \\
+		Sensitivity/Recall & 1.0000 & 0.7749 & N/A & 0.9056 & 0.0000 \\
+		Specificity & 1.0000 & 0.9183 & 1.0000 & 0.8904 & 1.0000 \\
+		Precision & 1.0000 & 0.8263 & N/A & 0.7908 & NaN \\
+		\hline
+	\end{tabular}
+	
+	\subsection* {2.2) Forest Results}
+	\textbf{Predictions (Confusion Table)}:\\
+	\begin{tabular}{|cc|ccccc|}
+		\hline
+		~ & ~ & Orig & ~ & ~ & ~ & ~ \\
+		~ & ~ & not\_rec & priority & rec & spec\_prior & very\_rec \\
+		\hline
+		Predic & not\_rec & 1623 & 0 & 0 & 0 & 0 \\
+		~ & priority & 0 & 1644 & 0 & 4 & 22 \\
+		~ & rec & 0 & 0 & 0 & 0 & 0 \\
+		~ & spec\_prior & 0 & 13 & 0 & 1553 & 0 \\
+		~ & very\_rec & 0 & 0 & 0 & 0 & 101 \\
+		\hline
+	\end{tabular}~\\
+	\\\\
+	\textbf{Stat Summary}:\\
+	Total Accuracy: 0.9921\\
+	\\
+	\begin{tabular}{|c|ccccc|}
+		\hline
+		~ & not\_rec & priority & rec & spec\_prior & ver\_rec \\
+		\hline
+		Balanced Accuracy & 1.0000 & 0.9921 & N/A & 0.9968 & 0.9106 \\
+		Sensitivity/Recall & 1.0000 & 0.9922 & N/A & 0.9974 & 0.8211 \\
+		Specificity & 1.0000 & 0.9921 & 1.0000 & 0.9962 & 1.0000 \\
+		Precision & 1.0000 & 0.9844 & N/A & 0.9917 & 1.0000 \\
+		\hline
+	\end{tabular}
+
+	
+	\section* {Part 2.3 - LED Dataset}
+	
+	\subsection* {2.3) Decison Tree Results}
+	\textbf{Predictions (Confusion Table)}:\\
+	\begin{tabular}{|cc|cccccccccc|}
+		\hline
+		~ & ~ & Orig & ~ & ~ & ~ & ~ & ~ & ~ & ~ & ~ & ~ \\
+		~ & ~ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\
+		\hline
+		Predic & 0 & 129 & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 16 & 1 \\
+		~ & 1 & 0 & 135 & 0 & 0 & 14 & 0 & 0 & 20 & 0 & 0 \\
+		~ & 2 & 3 & 3 & 142 & 9 & 0 & 0 & 0 & 2 & 5 & 1 \\
+		~ & 3 & 14 & 19 & 12 & 148 & 1 & 16 & 9 & 15 & 15 & 17 \\
+		~ & 4 & 0 & 16 & 0 & 1 & 144 & 3 & 0 & 0 & 1 & 15 \\
+		~ & 5 & 4 & 0 & 2 & 2 & 15 & 134 & 11 & 2 & 2 & 21 \\
+		~ & 6 & 11 & 1 & 1 & 0 & 0 & 15 & 148 & 0 & 15 & 2 \\
+		~ & 7 & 6 & 11 & 18 & 14 & 3 & 1 & 0 & 147 & 0 & 3 \\
+		~ & 8 & 16 & 0 & 14 & 3 & 11 & 3 & 17 & 0 & 153 & 16 \\
+		~ & 9 & 22 & 1 & 1 & 16 & 18 & 19 & 5 & 11 & 16 & 141 \\
+		\hline
+	\end{tabular}~\\
+	\\\\
+	\textbf{Stat Summary}:\\
+	Total Accuracy: 0.7105\\
+	\\
+	\begin{tabular}{|c|ccccc|}
+		\hline
+		~ & 0 & 1 & 2 & 3 & 4 \\
+		\hline
+		Balanced Accuracy & 0.8093 & 0.8516 & 0.8673 & 0.8508 & 0.8395 \\
+		Sensitivity/Recall & 0.6293 & 0.7219 & 0.7474 & 0.7668 & 0.6990 \\
+		Specificity & 0.9894 & 0.9812 & 0.9873 & 0.9347 & 0.9799 \\
+		Precision & 0.8716 & 0.7988 & 0.8606 & 0.5564 & 0.8000 \\
+		\hline
+	\end{tabular}\\
+	\begin{tabular}{|c|ccccc|}
+		\hline
+		~ & 5 & 6 & 7 & 8 & 9 \\
+		\hline
+		Balanced Accuracy & 0.8345 & 0.7789 & 0.7424 & 0.6861 & 0.6498 \\
+		Sensitivity/Recall & 0.7016 & 0.7789 & 0.7424 & 0.6861 & 0.6498 \\
+		Specificity & 0.9674 & 0.9751 & 0.9689 & 0.9550 & 0.9389 \\
+		Precision & 0.6943 & 0.7668 & 0.7241 & 0.6567 & 0.5640 \\
+		\hline
+	\end{tabular}
+	
+	\subsection* {2.3) Forest Results}
+	\textbf{Predictions (Confusion Table)}:\\
+	\begin{tabular}{|cc|cccccccccc|}
+		\hline
+		~ & ~ & Orig & ~ & ~ & ~ & ~ & ~ & ~ & ~ & ~ & ~ \\
+		~ & ~ & 0 & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 \\
+		\hline
+		Predic & 0 & 142 & 0 & 1 & 3 & 0 & 0 & 2 & 4 & 18 & 1 \\
+		~ & 1 & 1 & 167 & 0 & 7 & 24 & 1 & 0 & 22 & 0 & 2 \\
+		~ & 2 & 3 & 0 & 149 & 2 & 0 & 0 & 0 & 0 & 5 & 0 \\
+		~ & 3 & 1 & 5 & 21 & 147 & 1 & 13 & 1 & 12 & 13 & 18 \\
+		~ & 4 & 0 & 2 & 0 & 0 & 135 & 1 & 1 & 0 & 4 & 0 \\
+		~ & 5 & 4 & 0 & 2 & 2 & 3 & 133 & 11 & 0 & 2 & 21 \\
+		~ & 6 & 10 & 1 & 3 & 0 & 0 & 18 & 154 & 0 & 16 & 2 \\
+		~ & 7 & 8 & 11 & 2 & 12 & 5 & 2 & 0 & 158 & 0 & 4 \\
+		~ & 8 & 16 & 0 & 11 & 3 & 4 & 3 & 16 & 0 & 148 & 16 \\
+		~ & 9 & 20 & 1 & 1 & 17 & 34 & 20 & 5 & 2 & 17 & 153 \\
+		\hline
+	\end{tabular}~\\
+	\\\\
+	\textbf{Stat Summary}:\\
+	Total Accuracy: 0.7430\\
+	\\
+	\begin{tabular}{|c|ccccc|}
+		\hline
+		~ & 0 & 1 & 2 & 3 & 4 \\
+		\hline
+		Balanced Accuracy & 0.8383 & 0.9308 & 0.8893 & 0.8573 & 0.8254 \\
+		Sensitivity/Recall & 0.6927 & 0.8930 & 0.7842 & 0.7617 & 0.6553 \\
+		Specificity & 0.9838 & 0.9686 & 0.9945 & 0.9530 & 0.9955 \\
+		Precision & 0.8304 & 0.7455 & 0.9371 & 0.6336 & 0.9441 \\
+		\hline
+	\end{tabular}\\
+	\begin{tabular}{|c|ccccc|}
+		\hline
+		~ & 5 & 6 & 7 & 8 & 9 \\
+		\hline
+		Balanced Accuracy & 0.8357 & 0.8915 & 0.8868 & 0.8124 & 0.8197 \\
+		Sensitivity/Recall & 0.6963 & 0.8105 & 0.7980 & 0.6637 & 0.7051 \\
+		Specificity & 0.9751 & 0.9724 & 0.9756 & 0.9612 & 0.9344 \\
+		Precision & 0.7472 & 0.7549 & 0.7822 & 0.6820 & 0.5667 \\
+		\hline
+	\end{tabular}
+
+	\pagebreak
+	\section* {Part 2 Summary}
+	In all instances of Part 2, the Forest (ensemble method) seemed all around more accurate than the single Decision Tree method.\\
+	\\
+	In part 2.1, Forest even increased the accuracy to 100\%, which I think is pretty impressive. The Forest in Part 2.2 increased accuracy to nearly 100\%, with minimal errors.\\
+	\\
+	Meanwhile, the Forest in Part 2.3 only increased accuracy by a few percent, but it was still an overall improvement. I think this was likely due to the inherent noise of the dataset, and the Forest method most likely internalizing some of that noise.
+
+\end{document}
\ No newline at end of file
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diff --git a/documents/references.md b/documents/references.md
index 5b9d47f..596bc2d 100644
--- a/documents/references.md
+++ b/documents/references.md
@@ -10,6 +10,9 @@ All references to external logic. Includes anything from stack overflow links to
 ### Decision Trees
 <https://en.wikipedia.org/wiki/Decision_tree_learning>
 
+### Explanation of "Balanced Accuracy", plus Sensitivity and Specificity
+<https://statisticaloddsandends.wordpress.com/2020/01/23/what-is-balanced-accuracy/>
+
 
 ## R
 
-- 
GitLab