Step#$2-$ Upload the dataset

data $=$ pdread cssel'vertebrate.csv'header$=$'infer'$)$

data

Take screenshot of this output

Step#$3-$

Given the limited number of training examples, let's convert the problem into a binary classification task

$($mammals versus non$-$mammals$).$

$[$Student to code this themselves$]$

Take screenshot of the original data with the new classification output

Step#$4-$

We can apply Pandas cross$-$tabulation to examine the relationship between the Warm$-$blooded and Gives

Birth attributes with respect to the class.

od$,$crasstabl$[$data$[\text{'}$Warm$-$blooded'$],$data$[\text{'}$Gives Birth'$]],$data$[\text{'}$Class$\text{'}])$

Take screenshot of this output

Step#$5-$

In this section, we apply a decision tree classifier to the vertebrate dataset described in the previous

subsection. We're going to use a Python library called "sklearn" which also supports calculating 'gini' as the

impurity measure.

from sklearn import tree

$Y=$ data$[\text{'}$Class$\text{'}]$

X $=$ datardrop$([\text{'}$Name$\text{'},$'Class'$],$axis$=1)$

clf $=$ tree.DecisionTreeClassifier$($criterion$=$'entropy',max depth$=3)$

clf $=$clffiti$(x,Y)$

The preceding commands will extract the predictor $(x)$ and target class $(Y)$ attributes from the vertebrate

dataset and create a decision tree classifier object using entropy as its impurity measure for splitting

criterion. The classifier above is also constrained to generate trees with a maximum depth equals to $3.$ Next,

the classifier is trained on the labeled data using the fit$()$ function.