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Looking behind accuracy – precision
Looking behind accuracy – precision
and recall
Let us step back and think again what we are trying to achieve here. Actually, we
do not need a classifier that perfectly predicts good and bad answers, as we
measured it until now using accuracy. If we can tune the classifier to be particularly
good in predicting one class, we could adapt the feedback to the user accordingly.
If we had a classifier, for example, that was always right when it predicted an
answer to be bad, we would give no feedback until the classifier detected the
answer to be bad. Contrariwise, if the classifier succeeded in predicting answers to
be always good, we could show helpful comments to the user at the beginning and
remove them when the classifier said that the answer is a good one.
To find out which situation we are in here, we have to understand how to measure
precision and recall. To understand this, we have to look into the four distinct
classification results as they are described in the following table:
and recall
Let us step back and think again what we are trying to achieve here. Actually, we
do not need a classifier that perfectly predicts good and bad answers, as we
measured it until now using accuracy. If we can tune the classifier to be particularly
good in predicting one class, we could adapt the feedback to the user accordingly.
If we had a classifier, for example, that was always right when it predicted an
answer to be bad, we would give no feedback until the classifier detected the
answer to be bad. Contrariwise, if the classifier succeeded in predicting answers to
be always good, we could show helpful comments to the user at the beginning and
remove them when the classifier said that the answer is a good one.
To find out which situation we are in here, we have to understand how to measure
precision and recall. To understand this, we have to look into the four distinct
classification results as they are described in the following table:
0/5000
Nhìn đằng sau chính xác-chính xácvà nhớ lạiChúng ta hãy bước trở lại và suy nghĩ lại những gì chúng tôi đang cố gắng để đạt được ở đây. Trên thực tế, chúng tôikhông cần một loại hoàn toàn dự đoán câu trả lời tốt và xấu, như chúng tôiđo nó cho đến bây giờ bằng cách sử dụng chính xác. Nếu chúng tôi có thể điều chỉnh loại để đặc biệttốt trong dự đoán một lớp, chúng tôi có thể điều chỉnh thông tin phản hồi cho người dùng cho phù hợp.Nếu chúng tôi đã có một loại, ví dụ, đó là luôn luôn đúng khi nó dự đoán mộttrả lời để được xấu, chúng tôi sẽ cung cấp cho không có phản hồi cho đến khi loại phát hiện cáccâu trả lời là xấu. Ngốc, nếu loại đã thành công trong dự đoán trả lời đểluôn luôn được tốt, chúng tôi có thể hiển thị ý kiến hữu ích cho người dùng ở đầu vàloại bỏ chúng khi loại nói rằng câu trả lời là tốt nhất.Để tìm hiểu mà tình hình chúng tôi đang ở đây, chúng ta phải hiểu làm thế nào để đo lườngđộ chính xác và thu hồi. Để hiểu điều này, chúng tôi đã nhìn vào bốn riêng biệtphân loại kết quả như họ được mô tả trong bảng sau:
đang được dịch, vui lòng đợi..
Looking behind accuracy – precision
and recall
Let us step back and think again what we are trying to achieve here. Actually, we
do not need a classifier that perfectly predicts good and bad answers, as we
measured it until now using accuracy. If we can tune the classifier to be particularly
good in predicting one class, we could adapt the feedback to the user accordingly.
If we had a classifier, for example, that was always right when it predicted an
answer to be bad, we would give no feedback until the classifier detected the
answer to be bad. Contrariwise, if the classifier succeeded in predicting answers to
be always good, we could show helpful comments to the user at the beginning and
remove them when the classifier said that the answer is a good one.
To find out which situation we are in here, we have to understand how to measure
precision and recall. To understand this, we have to look into the four distinct
classification results as they are described in the following table:
and recall
Let us step back and think again what we are trying to achieve here. Actually, we
do not need a classifier that perfectly predicts good and bad answers, as we
measured it until now using accuracy. If we can tune the classifier to be particularly
good in predicting one class, we could adapt the feedback to the user accordingly.
If we had a classifier, for example, that was always right when it predicted an
answer to be bad, we would give no feedback until the classifier detected the
answer to be bad. Contrariwise, if the classifier succeeded in predicting answers to
be always good, we could show helpful comments to the user at the beginning and
remove them when the classifier said that the answer is a good one.
To find out which situation we are in here, we have to understand how to measure
precision and recall. To understand this, we have to look into the four distinct
classification results as they are described in the following table:
đang được dịch, vui lòng đợi..
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