決策樹VS隨機森林——應該使用哪種演算法？（附程式碼&連結）

# Data Preprocessing and null values imputation
# Label Encoding
df['Gender']=df['Gender'].map({'Male':1,'Female':0})
df['Married']=df['Married'].map({'Yes':1,'No':0})
df['Education']=df['Education'].map({'Graduate':1,'Not Graduate':0})
df['Dependents'].replace('3+',3,inplace=True)
df['Self_Employed']=df['Self_Employed'].map({'Yes':1,'No':0})
df['Property_Area']=df['Property_Area'].map({'Semiurban':1,'Urban':2,'Rural':3})
df['Loan_Status']=df['Loan_Status'].map({'Y':1,'N':0})
#Null Value Imputation
rev_null=['Gender','Married','Dependents','Self_Employed','Credit_History','LoanAmount','Loan_Amount_Term']
df[rev_null]=df[rev_null].replace({np.nan:df['Gender'].mode(),
                                   np.nan:df['Married'].mode(),
                                   np.nan:df['Dependents'].mode(),
                                   np.nan:df['Self_Employed'].mode(),
                                   np.nan:df['Credit_History'].mode(),
                                   np.nan:df['LoanAmount'].mean(),
                                   np.nan:df['Loan_Amount_Term'].mean()})rfc_vs_dt-2.py hosted with ❤ by GitHub

X=df.drop(columns=['Loan_ID','Loan_Status']).values
Y=df['Loan_Status'].values
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)rfc_vs_dt-3.py hosted with ❤ by GitHub

print('Shape of X_train=>',X_train.shape)
print('Shape of X_test=>',X_test.shape)
print('Shape of Y_train=>',Y_train.shape)
print('Shape of Y_test=>',Y_test.shape)rfc_vs_dt-4.py hosted with ❤ by GitHub

# Building Decision Tree
from sklearn.tree import DecisionTreeClassifier
dt = DecisionTreeClassifier(criterion = 'entropy', random_state = 42)
dt.fit(X_train, Y_train)
dt_pred_train = dt.predict(X_train)rfc_vs_dt-5.py hosted with ❤ by GitHub

# Evaluation on Training set
dt_pred_train = dt.predict(X_train)
print('Training Set Evaluation F1-Score=>',f1_score(Y_train,dt_pred_train))rfc_vs_dt-6.py hosted with ❤ by GitHub# Evaluating on Test set
dt_pred_test = dt.predict(X_test)
print('Testing Set Evaluation F1-Score=>',f1_score(Y_test,dt_pred_test))rfc_vs_dt-7.py hosted with ❤ by GitHub

 # Building  Random Forest Classifier
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(criterion = 'entropy', random_state = 42)
rfc.fit(X_train, Y_train)# Evaluating on Training set
rfc_pred_train = rfc.predict(X_train)
print('Training Set Evaluation F1-Score=>',f1_score(Y_train,rfc_pred_train))rfc_vs_dt-8.py hosted with ❤ by GitHubf1 score random forest# Evaluating on Test set
rfc_pred_test = rfc.predict(X_test)
print('Testing Set Evaluation F1-Score=>',f1_score(Y_test,rfc_pred_test))rfc_vs_dt-9.py hosted with ❤ by GitHub

feature_importance=pd.DataFrame({
    'rfc':rfc.feature_importances_,
    'dt':dt.feature_importances_
},index=df.drop(columns=['Loan_ID','Loan_Status']).columns)
feature_importance.sort_values(by='rfc',ascending=True,inplace=True)index = np.arange(len(feature_importance))
fig, ax = plt.subplots(figsize=(18,8))
rfc_feature=ax.barh(index,feature_importance['rfc'],0.4,color='purple',label='Random Forest')
dt_feature=ax.barh(index+0.4,feature_importance['dt'],0.4,color='lightgreen',label='Decision Tree')
ax.set(yticks=index+0.4,yticklabels=feature_importance.index)ax.legend()
plt.show()rfc_vs_dt-10.py hosted with ❤ by GitHub