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% Document Info
\author{lulu\_0012}
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  /Title (test-ml.pdf)
  /Creator (Cheatography)
  /Author (lulu\_0012)
  /Subject (test\_ml Cheat Sheet)
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\fancyhead[L]{
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{C}
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    \vspace{-7pt}
    {\parbox{\dimexpr\textwidth-2\fboxsep\relax}{\noindent
        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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\columnbreak
\begin{tabulary}{11cm}{L}
    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{test\_ml Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{lulu\_0012} via \textcolor{DarkBackground}{\uline{cheatography.com/35807/cs/11266/}}}
\end{tabulary}
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\fancyfoot[L]{ \footnotesize
\noindent
\begin{multicols}{3}
\begin{tabulary}{5.8cm}{LL}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}lulu\_0012 \\
  \uline{cheatography.com/lulu-0012} \\
  \end{tabulary}
\vfill
\columnbreak
\begin{tabulary}{5.8cm}{L}
  \SetRowColor{FootBackground}
  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Published 27th March, 2017.\\
   Updated 30th April, 2017.\\
   Page {\thepage} of \pageref{LastPage}.
\end{tabulary}
\vfill
\columnbreak
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Sponsor}}  \\
  \SetRowColor{white}
  \vspace{-5pt}
  %\includegraphics[width=48px,height=48px]{dave.jpeg}
  Measure your website readability!\\
  www.readability-score.com
\end{tabulary}
\end{multicols}}




\begin{document}
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\begin{multicols*}{3}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Three Common Types of Problem}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Regression}} To find the relationship between a dependent variable and many independent variables} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Classification}} To classify an observation to one of the several known catogories} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Clustering}} To group a set of objects into several unknown clusters} \tn 
% Row Count 6 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Regression}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{p{5.377cm}}{\vspace{1px}\centerline{\includegraphics[width=5.1cm]{/web/www.cheatography.com/public/uploads/lulu-0012_1490640627_Linear_regression.png}}} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{fa-rocket\}\}  {\bf{Model evaluation methods}}: \newline R\textasciicircum{}2\textasciicircum{}, Adjusted R\textasciicircum{}2\textasciicircum{}, MAE {\emph{(mean absolute error)}}, MSE {\emph{(mean square error)}}, RMSE {\emph{(root mean square error)}}, AIC {\emph{(Akaike information criterion)}}, BIC {\emph{(Bayesian information criterion)}}, Residual analysis, Goodness-of-fit test, Cross validation}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Classification}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{p{5.377cm}}{\vspace{1px}\centerline{\includegraphics[width=5.1cm]{/web/www.cheatography.com/public/uploads/lulu-0012_1490639284_ml_classification.jpg}}} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{fa-rocket\}\} {\bf{Model evaluation methods}} \newline Accuracy, Confusion matrix, Sensitivity and specificity, ROC {\emph{(receiver operating characteristic)}}, AUC {\emph{(area under the curve)}}, Cross validation}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{Clustering}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{p{5.377cm}}{\vspace{1px}\centerline{\includegraphics[width=5.1cm]{/web/www.cheatography.com/public/uploads/lulu-0012_1490576647_220px-Cluster-2.svg.png}}} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{\{\{fa-rocket\}\}  {\bf{Model evaluation methods}} \newline Models can be externally evaluated using data that are not used for clustering but with known class labels}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-key\}\} General steps to build a model}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{1. Collecting the data.} \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{2. Preparing the data and fixing issues such as missing values and outliers.} \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{3. Use exploratory analysis to help study the content of your data and select a proper algorithm that suits your need.} \tn 
% Row Count 6 (+ 3)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{4. Training a model using the algorithm you just selected. Start with a simple model that only uses the most important variables/features.} \tn 
% Row Count 9 (+ 3)
% Row 4
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{5. Check model performance using the evaluation methods.} \tn 
% Row Count 11 (+ 2)
% Row 5
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\mymulticolumn{1}{x{5.377cm}}{6. If the model is not satisfactory, choose another algorithm or introduce different variables into the exsiting model.} \tn 
% Row Count 14 (+ 3)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-key\}\} Popular tools of implementation}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{R}} ML libraries including {\emph{stats}}, {\emph{glmnet}}, {\emph{caret}}} \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Python}} popular packages for ML including {\emph{scikit-learn}}, {\emph{statsmodels}}} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Alteryx Designer}} 'drag-n-drop' and requires minimum coding} \tn 
% Row Count 6 (+ 2)
% Row 3
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{{\bf{Microsoft Azure Machine Learning Studio}} 'drag-n-drop' and requires minimum coding} \tn 
% Row Count 8 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.4885 cm} x{2.4885 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} Linear Regression}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Supervised \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & Regression \tn 
% Row Count 2 (+ 1)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Revenue prediction \tn 
% Row Count 3 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{Widely used for predicting numeric values (or quantities). It trains and predicts fast, but can be prone to overfitting so proper feature selection is often needed.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.09034 cm} x{2.88666 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} Logistic regression}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Supervised \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & Classification \tn 
% Row Count 3 (+ 1)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Customer churn prediction \tn 
% Row Count 5 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{A generalized linear model with dependent variable being binary (0-1). Mostly used to predict whether an event is going to occur based on the dependent variables.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.09034 cm} x{2.88666 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} Decision Tree}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Supervised \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & \seqsplit{Classification/Regression} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Targeted advertising \tn 
% Row Count 5 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{It requires little data preparation and can handle both numeric and categorical data. Easy to interpret and visualize but susceptible to overfitting.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{1.9908 cm} x{2.9862 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} Random Forest}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Supervised \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & \seqsplit{Classification/Regression} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Credit card fraud detection \tn 
% Row Count 6 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{An ensemble method that combines many decision trees together. It has all pros that a basic decision tree has, can handle many features and usually has high accuracy.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.23965 cm} x{2.73735 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} K-means}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Unsupervised \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & Clustering \tn 
% Row Count 2 (+ 1)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Customer segementation \tn 
% Row Count 3 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{This method groups objects into {\emph{k}} clusters. The goal is to have the objects in one cluster more similar to each other than to any object in other clusters. When {\emph{k}} is not pre-determined, many methods can be used to find a good value of {\emph{k}}, such as the elbow method and silhouette method.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{pagebreak}}  \tn
% Row 0
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{} \tn 
% Row Count 0 (+ 0)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.33919 cm} x{2.63781 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} Naïve Bayes}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Supervised \tn 
% Row Count 1 (+ 1)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & Classification \tn 
% Row Count 2 (+ 1)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Email spam filtering \tn 
% Row Count 3 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{A conditional probability model that assumes all features are conditionally independent on each other. Trains and predicts fast but the precision is low for small datasets and can suffer from 'zero-frequency' problem.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.09034 cm} x{2.88666 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} K-nearest Neighbors (KNN)}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Supervised \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & Classification \tn 
% Row Count 3 (+ 1)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Bank credit risk analysis \tn 
% Row Count 5 (+ 2)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{A lazy learning algorithm that doesn't require much in training, but can be slow in prediction if you have a large data set.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{x{2.09034 cm} x{2.88666 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{5.377cm}}{\bf\textcolor{white}{\{\{fa-plug\}\} Support Vector Machine (SVM)}}  \tn
% Row 0
\SetRowColor{LightBackground}
{\bf{Learning style}} & Supervised \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
{\bf{Problem}} & \seqsplit{Classification/Regression} \tn 
% Row Count 4 (+ 2)
% Row 2
\SetRowColor{LightBackground}
{\bf{Use case}} & Text classification \tn 
% Row Count 5 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\SetRowColor{LightBackground}
\mymulticolumn{2}{x{5.377cm}}{It uses some kernel function to map data points to a higher dimensional space and find a hyperplane to divide these points in that space. Ideal for very large data set with high dimensions, or if you know the decision boundary is not linear.}  \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{column break here}}  \tn
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\SetRowColor{LightBackground}
\mymulticolumn{1}{x{5.377cm}}{} \tn 
% Row Count 0 (+ 0)
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{5.377cm}{X}
\SetRowColor{DarkBackground}
\mymulticolumn{1}{x{5.377cm}}{\bf\textcolor{white}{References}}  \tn
\SetRowColor{white}
\mymulticolumn{1}{x{5.377cm}}{Basics of machine learning \newline % Row Count 1 (+ 1)
\seqsplit{https://www.analyticsvidhya.com/blog/2015/06/machine-learning-basics/} \newline % Row Count 3 (+ 2)
A practical guide to exploratory analysis \newline % Row Count 4 (+ 1)
\seqsplit{https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/} \newline % Row Count 6 (+ 2)
A cheat sheet of the libraries/modules of each algorithm in Python/R \newline % Row Count 8 (+ 2)
\seqsplit{http://www.dummies.com/programming/big-data/data-science/machine-learning-dummies-cheat-sheet/} \newline % Row Count 10 (+ 2)
A cheat sheet for using Microsoft Azure Machine Learning Studio \newline % Row Count 12 (+ 2)
\seqsplit{https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-cheat-sheet} \newline % Row Count 14 (+ 2)
Tool sheet of Alteryx Desinger \newline % Row Count 15 (+ 1)
\seqsplit{http://www.alteryx.com/sites/default/files/alteryx-designer-tools-sheet\_0.pdf}% Row Count 17 (+ 2)
} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}


% That's all folks
\end{multicols*}

\end{document}