Data Visualization Course

Assignment: Running a Classification Tree

15 Jan 2017

Program and outputs

Data loading and cleaning

import pandas as pandas
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
import sklearn.metrics
import numpy as np

CSV_PATH = 'gapminder.csv'

data = pandas.read_csv(CSV_PATH)
print('Total number of countries: {0}'.format(len(data)))

Total number of countries: 213

PREDICTORS = [
    'incomeperperson', 'alcconsumption', 'armedforcesrate',
    'breastcancerper100th', 'co2emissions', 'femaleemployrate',
    'hivrate', 'internetuserate', 'oilperperson',
    'polityscore', 'relectricperperson', 'suicideper100th',
    'employrate', 'urbanrate'
]

clean = data.copy()
    
clean = clean.replace(r'\s+', np.NaN, regex=True)
clean = clean[PREDICTORS + ['lifeexpectancy']].dropna()

for key in PREDICTORS + ['lifeexpectancy']:
    clean[key] = pandas.to_numeric(clean[key], errors='coerce', downcast='integer')
    
clean = clean.astype(int)

predictors = clean[PREDICTORS]
targets = clean.lifeexpectancy

pred_train, pred_test, tar_train, tar_test = train_test_split(predictors, targets, test_size=.4)

print(pred_train.shape, pred_test.shape, tar_train.shape, tar_test.shape)

(33, 14) (23, 14) (33,) (23,)

Modeling and output

# Build model on training data
classifier = DecisionTreeClassifier()
classifier = classifier.fit(pred_train, tar_train)

predictions = classifier.predict(pred_test)

print('Confusion matrix:', sklearn.metrics.confusion_matrix(tar_test, predictions), sep='\n')
print('Accuracy score:', sklearn.metrics.accuracy_score(tar_test, predictions))

Confusion matrix:
[[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
 [0 0 0 0 0 0 1 0 0 1 0 0 0 0 0]
 [1 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 1 0 0 1 0 0 0 0 0]
 [0 0 0 0 0 0 0 1 0 1 0 0 0 0 0]
 [1 0 0 0 0 0 1 2 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 1 0 0 0 0 0 0 0]
 [0 0 0 0 0 0 0 0 0 0 2 1 1 0 0]
 [0 0 0 0 0 0 0 0 0 0 0 1 3 1 0]
 [0 0 0 0 0 0 0 0 0 0 0 0 1 0 0]]
Accuracy score: 0.130434782609

# Displaying the decision tree
from sklearn import tree
from io import StringIO
from IPython.display import Image
import pydotplus

out = StringIO()
tree.export_graphviz(classifier, out_file=out)
graph = pydotplus.graph_from_dot_data(out.getvalue())
Image(graph.create_png())

png

Summary

I was able to create a decision tree out of the gapminder dataset evaluating the conditions that contribute to life expectancy. A lot of data cleanup was required to generate this tree, as sklearn couldn&rsqu;t build a tree out of floats, which comprised most of my dataset.

I think the gapminder dataset is too small to build a meaningful decision tree. The accuracy score was 0.13 and the initial sample split was 8 and 25. The output is also incredibly hard to read. It would be nice to be able to replace X[0] with something like X[incomeperperson].

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