#split dataset in features and target variable
feature_cols = ['RIAGENDR_0', 'RIDAGEYR', 'RIDRETH3_2', 'RIDRETH3_3', 'RIDRETH3_4', 'RIDRETH3_6', 'RIDRETH3_7', 'INDFMPIR', 'DMDMARTZ_1.0', 'DMDMARTZ_2.0', 'DMDMARTZ_3.0', 'DMDMARTZ_4.0', 'DMDMARTZ_6.0', 'DMDEDUC2', 'RFXT010', 'BMXWT', 'BMXBMI', 'URXUMA', 'LBDHDD', 'LBXFER', 'LBXGH', 'LBXBPB', 'LBXBCD', 'LBXBSE', 'LBXBMN', 'URXUBA', 'URXUCD', 'URXUCO', 'URXUCS', 'URXUMO', 'URXUMN', 'URXUPB', 'URXUSB', 'URXUSN', 'URXUTL', 'URXUTU']
X = data[feature_cols] # Features

scale = StandardScaler()
X = scale.fit_transform(X)

y = data['depre_score'] # Target variable


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% test


clf = DecisionTreeClassifier()
clf = clf.fit(X_train,y_train)

y_pred = clf.predict(X_test)

print(y_test)
print(y_pred)

confusion = metrics.confusion_matrix(y_test, y_pred)
print(confusion)

print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

recall_sensitivity = metrics.recall_score(y_test, y_pred, pos_label=1)
recall_specificity = metrics.recall_score(y_test, y_pred, pos_label=0)
print(recall_sensitivity, recall_specificity)

CodePudding user response：

You should check your data. Not your code.

It could be due to either:

1. Data leakage
2. There is a variable which is highly correlated to your label

Did you check that 'depre_score' is not left also in X?

CodePudding user response：

Why do you think you are doing something wrong? Perhaps your data are such that you can achieve a perfect classification... e.g., see this mushroom classification.

Having said that, it is also possible that there is some leakage in your data as specified by @gtomer. That means an exact point that is present in training set is available in your test set. You can do K-fold test on your data and see how it follows up with the accuracy. And secondly, use different classifiers too (it is better to use Random Forests compared to Decision Trees)