🍑Confusion Matrix
Chap06_Logistic Regression, SVM, 분류평가지표, GridSearch.pdf
Overview
Là một phương pháp đánh giá kết quả của những bài toán phân loại với việc xem xét cả những chỉ số về độ chính xác và độ bao quát của các dự đoán cho từng lớp
it is a performance measurement for machine learning classification problem where output can be two or more classes. It is a table with 4 different combinations of predicted and actual values.
1. Accuracy
Accuracy is used to measure the performance of the model. It is the ratio of Total correct instances to the total instance
Example:
- True Positive (TP): It is the total counts having both predicted and actual values are Dog.
- True Negative (TN): It is the total counts having both predicted and actual values are Not Dog.
- False Positive (FP): It is the total counts having prediction is Dog while actually Not Dog.
- False Negative (FN): It is the total counts having prediction is Not Dog while actually, it is Dog.
2. Recall
Recall: measures the effectiveness of a classification model in identifying all relevant instances from a dataset. It is the ratio of the number of true positive (TP) instances to the sum of true positive and false negative (FN) instances.
recall : 재현율, 애매한거 사용
3. Precision
Precision: is a measure of how accurate a model’s positive predictions are. It is defined as the ratio of true positive predictions to the total number of positive predictions made by the model
precision: 정밀도, 애매한거 사용 안함
4. F1 -score
F1-score is used to evaluate the overall performance of a classification model.
It is the harmonic mean of precision and recall
정밀도와 재현율의 조화평균
Example:
knn.fit(x_train,y_train)# knn model 사용
y_pred = knn.predict(x_test)from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
Explaination:
- recall : 재현율, 애매한거 사용
- precision: 정밀도, 애매한거 사용 안함
- f1-score: 정밀도와 재현율의 조화평균
- 솃다 0~1까지의 값을 갖는다, 1에 가까울수록 좋음
- support : 데이터 수
- accuracy: 전체중에 맞춘비율
- macro avg: 단순 평균
- weighted avg : 가중평균, 값이 많은 데이터가 높은 가중치를 갖는다 =
