Exercise$\_6.$ipynb

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M$6$: Exercise $6$

Montclair State Un

Home $-$ Google Dr

Exercise$\_6.$ipynb

Fit a logistic regres

Fit a decision tree

colab.research.google.com$/$drive$/1$reKpEkvaa$3$XULHEy$8$jTI$8$VWL$4$hKHSvvE#scrollTo$=$D$-$O$6$pUSwXGi$1$

Exercise$\_6.$ipynb

File Edit View Insert Runtime Tools Help All changes saved

$\backslash$table$[[+$ Code $+$ Text$],[6$s $[2],3,0\mathrm{.}480519,0\mathrm{.}105882,0\mathrm{.}133333,0\mathrm{.}148750,0\mathrm{.}000000,0\mathrm{.}0,0\mathrm{.}0,0,0,0,...,0],[**,,0\mathrm{.}467532,0\mathrm{.}079851,0\mathrm{.}133333,0\mathrm{.}172983,0\mathrm{.}016129,0\mathrm{.}0,0\mathrm{.}0,0,0,0,,0]]$

$5$ rows $43$ columns

Question $1$

Create x$\_$train, y$\_$train, x$\_$test, y$\_$test variables

Split the dataset into training and testing.

Assign the training input dataset to $x_{-}$train, assign the test input dataset to $x_{-}$test, assign the training output dataset to $y_{?}$train, assign the training output dataset to $y_{-}$test

The size of your training data should be $70\%$ of the size of the original dataset. The size of the test data should be $30\%$ of the original dataset

The output variable $($also known as the Y variable or the target variable$)$ in this dataset is deposit$\_$yes

os

$\text{'}\text{'}\text{'}$Your code goes in this cell.$\text{'}\text{'}\text{'}$

from sklearn.model$\_$selection import train$\_$test$\_$split

x $=$ df$\_$bank$\_$processed.drop$(\text{'}$deposit$\_$yes', axis$=1)$

y $=$ df$\_$bank$\_$processed$[\text{'}$deposit$\_$yes'$]$

x$\_$train, x$\_$test, y$\_$train, y$\_$test $=$ train$\_$test$\_$split$(x,y,$ test$\_$size $=0\mathrm{.}3,$ random$\_$state $=42$