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% Document Info
\author{DarioPittera (aggialavura)}
\pdfinfo{
  /Title (python-decision-tree-and-random-forest.pdf)
  /Creator (Cheatography)
  /Author (DarioPittera (aggialavura))
  /Subject (Python - Decision Tree \& Random Forest Cheat Sheet)
}

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        \hspace*{-6pt}\includegraphics[width=5.8cm]{/web/www.cheatography.com/public/images/cheatography_logo.pdf}}
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    \vspace{-2pt}\large{\bf{\textcolor{DarkBackground}{\textrm{Python - Decision Tree \& Random Forest Cheat Sheet}}}} \\
    \normalsize{by \textcolor{DarkBackground}{DarioPittera (aggialavura)} via \textcolor{DarkBackground}{\uline{cheatography.com/83764/cs/19971/}}}
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  \mymulticolumn{2}{p{5.377cm}}{\bf\textcolor{white}{Cheatographer}}  \\
  \vspace{-2pt}DarioPittera (aggialavura) \\
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  \mymulticolumn{1}{p{5.377cm}}{\bf\textcolor{white}{Cheat Sheet}}  \\
   \vspace{-2pt}Not Yet Published.\\
   Updated 1st July, 2019.\\
   Page {\thepage} of \pageref{LastPage}.
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  Measure your website readability!\\
  www.readability-score.com
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\begin{document}
\raggedright
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% Set font size to small. Switch to any value
% from this page to resize cheat sheet text:
% www.emerson.emory.edu/services/latex/latex_169.html
\footnotesize % Small font.

\begin{multicols*}{2}

\begin{tabularx}{8.4cm}{X}
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\mymulticolumn{1}{x{8.4cm}}{\bf\textcolor{white}{TO START}}  \tn
\SetRowColor{LightBackground}
\mymulticolumn{1}{x{8.4cm}}{\# IMPORT DATA LIBRARIES \newline import pandas as pd \newline import numpy as np \newline  \newline \# IMPORT VIS LIBRARIES \newline import matplotlib.pyplot as plt \newline import seaborn as sns \newline \%matplotlib inline \newline  \newline \# IMPORT MODELLING LIBRARIES \newline from sklearn.model\_selection import train\_test\_split \newline  \newline \# libraries for decision trees \newline from sklearn.tree import DecisionTreeClassifier \newline from sklearn.metrics import classification\_report,confusion\_matrix \newline  \newline \# libraries for random forest \newline from sklearn.ensemble import RandomForestClassifier \newline from sklearn.metrics import classification\_report,confusion\_matrix} \tn 
\hhline{>{\arrayrulecolor{DarkBackground}}-}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{5.36 cm} x{2.64 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{PRELIMINARY OPERATIONS}}  \tn
% Row 0
\SetRowColor{LightBackground}
df = pd.read\_csv('data.csv') & import data \tn 
% Row Count 2 (+ 2)
% Row 1
\SetRowColor{white}
sns.pairplot(df,hue='col') & pairplot \tn 
% Row Count 3 (+ 1)
% Row 2
\SetRowColor{LightBackground}
df.info() & check info df \tn 
% Row Count 4 (+ 1)
% Row 3
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df.describe() & check stats df \tn 
% Row Count 6 (+ 2)
% Row 4
\SetRowColor{LightBackground}
df.head() & check head df \tn 
% Row Count 7 (+ 1)
\hhline{>{\arrayrulecolor{DarkBackground}}--}
\end{tabularx}
\par\addvspace{1.3em}

\begin{tabularx}{8.4cm}{x{4.64 cm} x{3.36 cm} }
\SetRowColor{DarkBackground}
\mymulticolumn{2}{x{8.4cm}}{\bf\textcolor{white}{TRAIN MODEL - DECISION TREES}}  \tn
% Row 0
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\mymulticolumn{2}{x{8.4cm}}{{\bf{\{\{fa-columns\}\} SPLIT DATASET}}} \tn 
% Row Count 1 (+ 1)
% Row 1
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X = df{[}{[}'col1','col2',etc.{]}{]} & create df features \tn 
% Row Count 3 (+ 2)
% Row 2
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y = df{[}'col'{]} & create df var to predict \tn 
% Row Count 5 (+ 2)
% Row 3
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X\_train, X\_test, y\_train, y\_test = \{\{nl\}\} train\_test\_split(\{\{nl\}\}~~X,\{\{nl\}\}~~y, \{\{nl\}\}~~test\_size=0.3) & split df in train and test df \tn 
% Row Count 11 (+ 6)
% Row 4
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\mymulticolumn{2}{x{8.4cm}}{{\bf{\{\{fa-signal\}\} FIT THE MODEL}}} \tn 
% Row Count 12 (+ 1)
% Row 5
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tree = \seqsplit{DecisionTreeClassifier()} & instatiate model \tn 
% Row Count 14 (+ 2)
% Row 6
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tree.fit(X\_train, y\_train) & train/fit the model \tn 
% Row Count 16 (+ 2)
% Row 7
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% Row 8
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pred = tree.predict(X\_test) & make predictions \tn 
% Row Count 19 (+ 2)
% Row 9
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% Row Count 20 (+ 1)
% Row 10
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\mymulticolumn{2}{x{8.4cm}}{\seqsplit{print(classification\_report(y\_test},pred))} \tn 
% Row Count 21 (+ 1)
% Row 11
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\mymulticolumn{2}{x{8.4cm}}{\seqsplit{print(confusion\_matrix(y\_test},pred))} \tn 
% Row Count 22 (+ 1)
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% Row 0
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% Row Count 1 (+ 1)
% Row 1
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X = df{[}{[}'col1','col2',etc.{]}{]} & create df features \tn 
% Row Count 3 (+ 2)
% Row 2
\SetRowColor{LightBackground}
y = df{[}'col'{]} & create df var to predict \tn 
% Row Count 5 (+ 2)
% Row 3
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X\_train, X\_test, y\_train, y\_test = \{\{nl\}\} train\_test\_split(\{\{nl\}\}~~X,\{\{nl\}\}~~y, \{\{nl\}\}~~test\_size=0.3) & split df in train and test df \tn 
% Row Count 11 (+ 6)
% Row 4
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% Row Count 12 (+ 1)
% Row 5
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rfc = RandomForestClassifier\{\{nl\}\}(n\_estimators=200)* & instatiate model \tn 
% Row Count 15 (+ 3)
% Row 6
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rfc.fit(X\_train, y\_train) & train/fit the model \tn 
% Row Count 17 (+ 2)
% Row 7
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% Row Count 18 (+ 1)
% Row 8
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rfc\_pred = rfc.predict(X\_test) & make predictions \tn 
% Row Count 20 (+ 2)
% Row 9
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% Row Count 21 (+ 1)
% Row 10
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% Row Count 22 (+ 1)
% Row 11
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% Row Count 23 (+ 1)
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\mymulticolumn{2}{x{8.4cm}}{{\bf{n\_estimators}}: number of trees to be used in the forest.}  \tn 
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\par\addvspace{1.3em}


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

\end{document}