Decision Tree - sklearn - python2.7 -spyder Ide

@paras1996 wrote:

Getting abnormal decision tree classifier accuracy 1.0 and only one element in confusion matrix.Also getting only one node in decision tree output. Help me by pointing my mistakes.

Code :

  import pandas
  import numpy
  from sklearn.cross_validation import train_test_split
  from sklearn.tree import DecisionTreeClassifier
  import sklearn.metrics

  data = pandas.read_csv('nesarc_pds.csv', low_memory=False)

#Setting variable to numeric.
data['CONSUMER'] = pandas.to_numeric(data['CONSUMER'], errors='coerce')
data['S2AQ16A'] = pandas.to_numeric(data['S2AQ16A'], errors='coerce')
data['S2DQ3C1'] = pandas.to_numeric(data['S2DQ3C1'], errors='coerce')
data['S2DQ3C2'] = pandas.to_numeric(data['S2DQ3C2'], errors='coerce')
data['S2DQ4C1'] = pandas.to_numeric(data['S2DQ4C1'], errors='coerce')
data['S2DQ4C2'] = pandas.to_numeric(data['S2DQ4C2'], errors='coerce')
data['S2DQ1'] = pandas.to_numeric(data['S2DQ1'], errors='coerce')
data['S2DQ2'] = pandas.to_numeric(data['S2DQ2'], errors='coerce')
data['SEX'] = pandas.to_numeric(data['SEX'], errors='coerce')

 #subset data to the age 10 to 30 when started drinking
 sub1=data[((data['S2AQ16A']>=10) & (data['S2AQ16A']<=30))]
 #Copy new DataFrame
sub2 = sub1.copy()

#Recording missing data
 sub2['S2AQ16A'] = sub2['S2AQ16A'].replace(99, numpy.nan)
 sub2['S2DQ3C1'] = sub2['S2DQ3C1'].replace(99, numpy.nan)
 sub2['S2DQ3C2'] = sub2['S2DQ3C2'].replace(9, numpy.nan)
 sub2['S2DQ4C1'] = sub2['S2DQ4C1'].replace(99, numpy.nan)
 sub2['S2DQ4C2'] = sub2['S2DQ4C2'].replace(9, numpy.nan)
 sub2['S2DQ1'] = sub2['S2DQ1'].replace(9, numpy.nan)
 sub2['S2DQ2'] = sub2['S2DQ2'].replace(9, numpy.nan)


  #creating a secondary variable for calculating sibling number.
  sub2['SIBNO'] = sub2['S2DQ3C1'] + sub2['S2DQ4C1']

#defining new variable for sibling drinking status by combining data of brothers and sisters
def SIBSTS(row):
if any([row['S2DQ3C2'] == 1, row['S2DQ4C2'] == 1]) :
    return 1
elif all([row['S2DQ3C2'] == 2, row['S2DQ4C2'] == 2]) :
    return 0
else :
    return numpy.nan
sub2['SIBSTS'] = sub2.apply(lambda row: SIBSTS (row),axis=1)

#defining new variable for parent status status of drinking
def PRSTS(row):
    if any([row['S2DQ1'] == 1, row['S2DQ2'] == 1]) :
        return 1
    elif all([row['S2DQ1'] == 2, row['S2DQ2'] == 2]) :
        return 0
   else :
        return numpy.nan
   sub2['PRSTS'] = sub2.apply(lambda row: PRSTS (row),axis=1)


  #recoding values for 'CONSUMER' into a new variable, DRSTS
  recode1 = {1: 1, 2: 1, 3: 0}
  sub2['DRSTS']= sub2['CONSUMER'].map(recode1)

 #recoding new values for SEX variable
 recode2 = {1: 1, 2: 0}
 sub2['GEN']= sub2['SEX'].map(recode2)

 data_clean = sub2.dropna()

 data_clean.dtypes
 data_clean.describe()

 #Modeling and Prediction

 #Split into training and testing sets

 predictors = data_clean[['S2AQ16A','SIBNO','SIBSTS','PRSTS','GEN']]

 targets = data_clean['DRSTS']

 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_train)

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

 #Displaying the decision tree
 from sklearn import tree
 #from StringIO import StringIO
 import io
 #from StringIO import StringIO
 from IPython.display import Image
 out = io.BytesIO()
 tree.export_graphviz(classifier, out_file=out)
 import pydotplus
 graph=pydotplus.graph_from_dot_data(out.getvalue())
 Image(graph.create_png())
 graph.write_pdf("iris.pdf")<img

output : the only node
src="/uploads/analyticsvidhya/original/2X/e/efa3cd9badf3cdbad4d04740d50e83262091c3fc.PNG" width="559" height="301">

datasheet of code : https://drive.google.com/open?id=0B61fgGvJIlfibXJCaTR3NVp2UGs

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