I am practicing with this life expectancy dataset from Kaggle (https://www.kaggle.com/datasets/kumarajarshi/life-expectancy-who?select=Life+Expectancy+Data.csv) and I want to train and visualize a classification and regression tree model. however, I keep getting an error that says "InvocationException: GraphViz's executables not found". I am wondering if this is because of the nature of the continuous numerical target dataset type? how can I visualize the model?
code:
import warnings
warnings.filterwarnings('ignore')
import pandas as pd
import numpy as np
import seaborn as sn
from sklearn import datasets
from sklearn import metrics
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeRegressor
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.tree import export_graphviz
import matplotlib.pyplot as plt,pydotplus
from IPython.display import Image,display
data = pd.read_csv('Life Expectancy Data.csv')
data = data.dropna(how = 'any')
#feature selection
data = data.drop(columns=['infant deaths', ' thinness 5-9 years', 'Alcohol', 'percentage expenditure', 'Hepatitis B', 'Total expenditure', 'Population', ' thinness 5-9 years', 'Year', 'Country'])
# Creating a instance of label Encoder.
le = LabelEncoder()
# Using .fit_transform function to fit label
# encoder and return encoded label
label = le.fit_transform(data['Status'])
# removing the column 'Status' from df
data.drop('Status', axis=1, inplace=True)
# Appending the array to our dataFrame
# with column name 'Status'
data['Status'] = label
#training model
model_data = data
X = data.drop(columns=['Life expectancy '])
y = data['Life expectancy ']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
model = DecisionTreeRegressor()
model.fit(X_train, y_train)
#visualizing tree
LEtree = tree.export_graphviz(model,
feature_names = ['Adult Mortality', 'Measles', ' BMI', 'under-five deaths', 'Polio', 'Diphtheria', ' HIV/AIDS', 'GDP', ' thinness 1-19 years', 'Income composition of resources', 'Schooling', 'Status'],
class_names = y,
label = 'all',
rounded = True,
filled = True)
graph=pydotplus.graph_from_dot_data(LEtree)
display(Image(graph.create_png()))
full error message:
InvocationException Traceback (most recent call last)
Input In [27], in <cell line: 2>()
1 graph=pydotplus.graph_from_dot_data(LEtree)
----> 2 display(Image(graph.create_png()))
File ~\Anaconda3\lib\site-packages\pydotplus\graphviz.py:1797, in Dot.__init__.<locals>.<lambda>(f, prog)
1792 # Automatically creates all the methods enabling the creation
1793 # of output in any of the supported formats.
1794 for frmt in self.formats:
1795 self.__setattr__(
1796 'create_' + frmt,
-> 1797 lambda f=frmt, prog=self.prog: self.create(format=f, prog=prog)
1798 )
1799 f = self.__dict__['create_' + frmt]
1800 f.__doc__ = (
1801 '''Refer to the docstring accompanying the'''
1802 ''''create' method for more information.'''
1803 )
File ~\Anaconda3\lib\site-packages\pydotplus\graphviz.py:1959, in Dot.create(self, prog, format)
1957 self.progs = find_graphviz()
1958 if self.progs is None:
-> 1959 raise InvocationException(
1960 'GraphViz\'s executables not found')
1962 if prog not in self.progs:
1963 raise InvocationException(
1964 'GraphViz\'s executable "%s" not found' % prog)
InvocationException: GraphViz's executables not found
