Error appears when try to calculate TP AND FP by using Confusion matrix
发布于2023-03-03 03:05 阅读(1313) 评论(0) 点赞(0) 收藏(1)
I build ML MODLE to find the accuracy of detection,
I faced below issue when try to find the TP rate and FP rate of each algorithm :
nnnnnnnnnnnnnnn
models = [RandomForestClassifier,DecisionTreeClassifier,KNeighborsClassifier,SGDClassifier,
GaussianNB,SVC, LogisticRegression]
accuracy_test=[]
model = []
for m in models:
model_name = type(m()).__name__
print('######-Model =>\033[07m {} \033[0m'.format(type(m()).__name__))
model_ = m()
model_.fit(X_train, y_train)
pred = model_.predict(X_test)
acc = accuracy_score(pred, y_test)
accuracy_test.append(acc)
model.append(model_name)
print('Test Accuracy :\033[32m \033[01m {:.5f}% \033[30m \033[0m'.format(acc*100))
print('\033[01m Classification_report \033[0m')
print(classification_report(y_test, pred))
group_names = ['True Neg','False Pos','False Neg','True Pos']
group_counts = ["{0:0.0f}".format(value) for value in
cf_matrix.flatten()]
group_percentages = ["{0:.2%}".format(value) for value in
cf_matrix.flatten()/np.sum(cf_matrix)]
labels = [f"{v1}\n{v2}\n{v3}" for v1, v2, v3 in
zip(group_names,group_counts,group_percentages)]
labels = np.asarray(labels).reshape(2,2)
sns.heatmap(cf_matrix, annot=labels, fmt='', cmap='Blues')
print('\033[01m Confusi``your text``on_matrix \033[0m')
cf_matrix = confusion_matrix(y_test, pred)
plot_ = sns.heatmap(cf_matrix/np.sum(cf_matrix), annot=True,fmt= '0.2%')
plt.show()
print('\033[31m###################- End -###################\033[0m')
this the error appears
File "<ipython-input-33-691c86584deb>", line 29
cf_matrix = confusion_matrix(y_test, pred)
^
IndentationError: unexpected indent
解决方案
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