How to optimize function built on top of the classifier?
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I have a dataset with classification model build for it for $n$ classes as target. And also using the probabilities for each class, which classificator returns, I built confidence function for each class that specify how confident is the model about predicted class given the predicted probabilities for each class and accuracy for each class.
Now, the problem is to select a subset of out dataset with values of confidence ($C_1, C_2..C_n$) higher than certain limit for each class, so that overall recall ($R$) for our whole test dataset with multiple classes wasn't lower than certain value (say, 0.95) and also the number of elements ($N)$ in subset were as high as possible, so basically we need to find: $$argmax(C_1, C_2,..C_n, R=const)$$
I have an idea to use something like RandomizedSearchCV treating $C_1, C_2..C_n$ as hyperpameters with specified ranges. But the problem is how to find all elements with with $R$ higher than specified.
classification optimization probability
asked 13 hours ago
DmytroSytroDmytroSytro
1387
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add a comment |
$begingroup$
I have a dataset with classification model build for it for $n$ classes as target. And also using the probabilities for each class, which classificator returns, I built confidence function for each class that specify how confident is the model about predicted class given the predicted probabilities for each class and accuracy for each class.
Now, the problem is to select a subset of out dataset with values of confidence ($C_1, C_2..C_n$) higher than certain limit for each class, so that overall recall ($R$) for our whole test dataset with multiple classes wasn't lower than certain value (say, 0.95) and also the number of elements ($N)$ in subset were as high as possible, so basically we need to find: $$argmax(C_1, C_2,..C_n, R=const)$$
I have an idea to use something like RandomizedSearchCV treating $C_1, C_2..C_n$ as hyperpameters with specified ranges. But the problem is how to find all elements with with $R$ higher than specified.
classification optimization probability
asked 13 hours ago
DmytroSytroDmytroSytro
1387
$endgroup$
add a comment |
$begingroup$
I have a dataset with classification model build for it for $n$ classes as target. And also using the probabilities for each class, which classificator returns, I built confidence function for each class that specify how confident is the model about predicted class given the predicted probabilities for each class and accuracy for each class.
Now, the problem is to select a subset of out dataset with values of confidence ($C_1, C_2..C_n$) higher than certain limit for each class, so that overall recall ($R$) for our whole test dataset with multiple classes wasn't lower than certain value (say, 0.95) and also the number of elements ($N)$ in subset were as high as possible, so basically we need to find: $$argmax(C_1, C_2,..C_n, R=const)$$
I have an idea to use something like RandomizedSearchCV treating $C_1, C_2..C_n$ as hyperpameters with specified ranges. But the problem is how to find all elements with with $R$ higher than specified.
classification optimization probability
asked 13 hours ago
DmytroSytroDmytroSytro
1387
$endgroup$
I have a dataset with classification model build for it for $n$ classes as target. And also using the probabilities for each class, which classificator returns, I built confidence function for each class that specify how confident is the model about predicted class given the predicted probabilities for each class and accuracy for each class.
Now, the problem is to select a subset of out dataset with values of confidence ($C_1, C_2..C_n$) higher than certain limit for each class, so that overall recall ($R$) for our whole test dataset with multiple classes wasn't lower than certain value (say, 0.95) and also the number of elements ($N)$ in subset were as high as possible, so basically we need to find: $$argmax(C_1, C_2,..C_n, R=const)$$
I have an idea to use something like RandomizedSearchCV treating $C_1, C_2..C_n$ as hyperpameters with specified ranges. But the problem is how to find all elements with with $R$ higher than specified.
classification optimization probability
classification optimization probability
asked 13 hours ago
DmytroSytroDmytroSytro
1387
asked 13 hours ago
DmytroSytroDmytroSytro
1387
edited 13 hours ago
DmytroSytro
asked 13 hours ago
DmytroSytroDmytroSytro
1387
asked 13 hours ago
DmytroSytroDmytroSytro
1387
asked 13 hours ago
DmytroSytroDmytroSytro
1387
1387
add a comment |
add a comment |
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