决策树sklearn：预测准确率100％

Question

我得到决策树分类器精度1.0，决策树输出中只有一个节点也只有混淆矩阵中的一个元素。 Random Forest也有类似的问题。

  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_test)

 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")

输出：

代码中使用的数据集 - nesar_pds

Answer 1

在训练数据集上构建模型后，您应该使用Test数据集来预测分类器的准确度。

此行中出现错误predictions=classifier.predict(pred_train)

应该是：predictions=classifier.predict(pred_test)

Answer 2

在print(sklearn.metrics.accuracy_score(tar_test, predictions))中，将其用作print(sklearn.metrics.accuracy_score(tar_test, predictions, normalize = False))。根据{{​​3}}，它说：'如果为假，则返回正确分类的样本数。否则，返回正确分类的样本的分数。在该结果中，正确预测的样本的数量与分离测试的目标的数量相同。然后，也许算法正确预测（真正奇怪的是）。