multiclass classification model

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showing results for - "multiclass classification model"
Bryanna
23 Nov 2016
1knn=KNeighborsClassifier()
2svc=SVC()
3lr=LogisticRegression()
4dt=DecisionTreeClassifier()
5gnb=GaussianNB()
6rfc=RandomForestClassifier()
7xgb=XGBClassifier()
8gbc=GradientBoostingClassifier()
9ada=AdaBoostClassifier()
10-------------------------------------------------------------------
11models=[]
12models.append(('KNeighborsClassifier',knn))
13models.append(('SVC',svc))
14models.append(('LogisticRegression',lr))
15models.append(('DecisionTreeClassifier',dt))
16models.append(('GaussianNB',gnb))
17models.append(('RandomForestClassifier',rfc))
18models.append(('XGBClassifier',xgb))
19--------------------------------------------------------------------
20
21from sklearn.metrics import classification_report,confusion_matrix,accuracy_score,roc_curve,auc,recall_score
22--------------------------------------------------------------------
23Model=[]
24score=[]
25cv=[]
26
27for name,model in models:
28    print('*****************',name,'*******************')
29    print('\n')
30    Model.append(name)
31    model.fit(x_train,y_train)
32    print(model)
33    pre=model.predict(x_test)
34    print('\n')
35    AS=accuracy_score(y_test,pre)
36    print('Accuracy_score  -',AS)
37    score.append(AS*100)
38    print('\n')
39    sc=cross_val_score(model,x,y,cv=10,scoring='accuracy').mean()
40    print('cross_val_score  -',sc)
41    cv.append(sc*100)
42    print('\n')
43        
44    print('classification report\n',classification_report(y_test,pre))
45    print('\n')
46    cm=confusion_matrix(y_test,pre)
47    print(cm)
48    print('\n')
49    plt.figure(figsize=(10,40))
50    plt.subplot(911)
51    plt.title(name)
52    print(sns.heatmap(cm,annot=True))
53    
54    
55 ---------------------------------------------------------------------------
56result=pd.DataFrame({'Model':Model,'Accuracy_score':score,'Cross_val_score':cv})
57result
58
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