Running a Classification Tree (Python)

Introduction:

Dataset: gapminder.csv

Predictors: 'internetuserate','urbanrate','employrate','lifeexpectancy'

Targets: polityscore

"polityscore" reflects the democracy level of a country. The score ranges from -10 to 10. 10 marks means the country is the most democratic. I divided it into 2 levels :[-10,0),[0,10),which return as 0 and 1 respectively,

Results:
Data Partitioning:

-predictors in training dataset: 4 variables and 91 observations
-predictors in test dataset: 4 variables and 61 observations
-target in training dataser: 1 variable and 91 observations
-target in test dataset: 1 variable and 61 observations

Training-test ratio: 0.6

Confusion matrix for the target_test sample:
[[ 6, 18],
 [ 9, 28]]

True Negative=6
True Positive =28
False Negative =9
False Positive=18

Accuracy=0.5901639344262295

Binary Decision  Tree:


Python Code:
from pandas import Series, DataFrame
import pandas as pd
import numpy as np

import matplotlib.pylab as plt
from sklearn.cross_validation import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import classification_report
import sklearn.metrics



data = pd.read_csv("gapminder.csv")
data['polityscore'] = data['polityscore'].convert_objects(convert_numeric=True)
data['internetuserate'] = data['internetuserate'].convert_objects(convert_numeric=True)
data['urbanrate'] = data['urbanrate'].convert_objects(convert_numeric=True)
data['employrate'] = data['employrate'].convert_objects(convert_numeric=True)
data['lifeexpectancy'] = data['lifeexpectancy'].convert_objects(convert_numeric=True)

data_clean = data.dropna()
data_clean.dtypes
data_clean.describe()

def politysco (row):

   if row['polityscore'] <= 0 :
      return 0
   elif row['polityscore'] <= 10:
      return 1
 
data_clean['polityscore'] = data_clean.apply (lambda row: politysco (row),axis=1)


predictors = data_clean[['internetuserate','urbanrate','employrate','lifeexpectancy',]]

targets =data_clean['polityscore']

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

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

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

predictions=classifier.predict(pred_test)

sklearn.metrics.confusion_matrix(tar_test,predictions)
sklearn.metrics.accuracy_score(tar_test, predictions)

#Displaying the decision tree
from sklearn import tree
#from StringIO import StringIO
from io import StringIO
#from StringIO import StringIO
from IPython.display import Image
out = StringIO()
tree.export_graphviz(classifier, out_file=out)

import pydotplus
graph=pydotplus.graph_from_dot_data(out.getvalue())
Image(graph.create_png())