🌲 Decision Tree Visualization

👨‍🏫 Vikesh K
📓 Lab-08

💡 “However difficult life may seem, there is always something you can do and succeed at.-Stephen Hawking” 💡

📝Lab Agenda

• Plotting of Decision Tree Plots

Importing modules and data

import pandas as pd
import matplotlib.pyplot as plt


from sklearn import tree
from sklearn.tree import DecisionTreeClassifier

from sklearn.model_selection import train_test_split

from sklearn.model_selection import GridSearchCV
import numpy as np

import warnings
warnings.filterwarnings('ignore')

df = pd.read_csv("https://raw.githubusercontent.com/vkoul/data/main/misc/titanic.csv")

df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

Data Prep

We will focus only on Numerical columns to keep the analysis straightforward

num_cols = df.select_dtypes(["int", "float"]).drop(columns = ['PassengerId']).columns.to_list()

df_num = df[num_cols].copy()

Filling in the missing value with median age

df_num.isna().sum()

Survived      0
Pclass        0
Age         177
SibSp         0
Parch         0
Fare          0
dtype: int64

median_age = df_num['Age'].median()

df_num['Age'] = df_num['Age'].fillna(median_age)

Prepare the X and y data

x = df_num.drop(columns = ['Survived'])
y = df_num['Survived']

Model Fitting

dt = DecisionTreeClassifier(max_depth = 2) # using depth of 2 to plot it properly

dt.fit(x, y)

DecisionTreeClassifier(max_depth=2)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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Plotting the decision tree

• https://scikit-learn.org/stable/modules/generated/sklearn.tree.plot_tree.html

• https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py

# checking the depth
dt.tree_.max_depth

2

features = list(x.columns) # save the column names as features

Decision Tree Plotting

plt.figure(figsize=(20,10))

tree.plot_tree(dt
               , feature_names = features # name of the features
               , max_depth = 5
               , filled= True # for color
               , fontsize= 9
               , node_ids = True # show the node number
               , class_names= ["Not", "Survived"]) # Names of each of the target classes in ascending numerical order.
# show the plot
plt.show()

The chart is read, top to bottom, from left to right. Hence the Nodes are also ordered in the same manner

Verification of the numbers based on the chart

df_num.query("Pclass <= 2.5").shape[0]

400

df_num.query("Pclass <= 2.5 & Parch <= 0.5").shape[0]

297

# df_num.query("Pclass <= 2.5 & Parch <= 0.5")

df_num.query("Pclass <= 2.5 & Parch > 0.5").shape[0]

103

df_num.query("Parch <= 2.5").shape[0]

876

y.value_counts()

Survived
0    549
1    342
Name: count, dtype: int64