Presiction time in Classification Lerner

11 次查看（过去 30 天）
EK
EK 2021-2-8
编辑： EK 2021-2-17
I would like to mesure the time that classifier takes for prediction. I see that in Classification Lerner toolbox there is parameter prediction speed obs/sec. Can anyone explain what does the obs/sec mean? How can I convert it in to time? Is it possible to calculate prediction time only for correct classifications?

请先登录，再回答此问题。

回答（1 个）

Aditya Patil
Aditya Patil 2021-2-15
obs/sec refers to number of observations processed per second. It's inverse would be the time taken for one prediction in seconds.
To calculate time taken for only the correct predictions, you can pass only the correctly predicted observations to the predict function, and calculate the time it takes for it to complete. You can find this option under Editor -> Run -> Run and Time
  3 个评论
显示 1更早的评论
Aditya Patil
Aditya Patil 2021-2-16
Currently, it's not possible to do so using the app. Can you elaborate on the use case? Generally for most models, there won't be any difference in time between correct and incorrect predictions. Even for models where it exists, it should be insignificant.
EK
EK 2021-2-17
编辑：EK 2021-2-17
I have a several tasks with the different degree of difficulty. I would like to access the task difficulty by mesuaring accuracy and classification speed of the LDA classifier. As expected when task difficulty increased the accuracy of the classifier decreased and the training time increased. I would expect the same relationship in prediction speed. But prediction speed was not different in all condition. I thought that it is because in the test I have many trials with the accuracy of classifier below chance and therefore the prediction time may not related to my task. I thought If I could sort out the trials leaving only correct predictions the prediction time would more precise reflect the difficulty of the task.

请先登录，再进行评论。

类别

在 Help Center 和 File Exchange 中查找有关 Get Started with Statistics and Machine Learning Toolbox 的更多信息

标签

产品


版本

R2019b

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by