How can I improve the accuracy of my confusion matrix to 100%? [on hold] The 2019 Stack Overflow Developer Survey Results Are In Unicorn Meta Zoo #1: Why another podcast? Announcing the arrival of Valued Associate #679: Cesar Manara 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsImprove k-means accuracyCan training label confidence be used to improve prediction accuracy?How to improve my test accuracy using CNN in TensorflowTrain Accuracy vs Test Accuracy vs Confusion matrixImprove test accuracy for TensorFlow CNNHow to best estimate the coefficients of a confusion matrix in case of strong class imbalance?How can I improve the accuracy of my neural network on a very unbalanced dataset?Why 100% accuracy on test data is not good?How to build confusion matrix , when predicted value and actual value is in sentence?How to reach continue training in xgboost
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How can I improve the accuracy of my confusion matrix to 100%? [on hold]
The 2019 Stack Overflow Developer Survey Results Are In
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsImprove k-means accuracyCan training label confidence be used to improve prediction accuracy?How to improve my test accuracy using CNN in TensorflowTrain Accuracy vs Test Accuracy vs Confusion matrixImprove test accuracy for TensorFlow CNNHow to best estimate the coefficients of a confusion matrix in case of strong class imbalance?How can I improve the accuracy of my neural network on a very unbalanced dataset?Why 100% accuracy on test data is not good?How to build confusion matrix , when predicted value and actual value is in sentence?How to reach continue training in xgboost
$begingroup$
Is there a possibility of attaining the above? Can someone share with me how to go about doing it if it is?
machine-learning python
edited yesterday
Community♦
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asked yesterday
RenaeRenae
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Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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put on hold as too broad by Mark.F, Dawny33♦ yesterday
Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
add a comment |
$begingroup$
Is there a possibility of attaining the above? Can someone share with me how to go about doing it if it is?
machine-learning python
edited yesterday
Community♦
1
asked yesterday
RenaeRenae
164
New contributor
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
put on hold as too broad by Mark.F, Dawny33♦ yesterday
Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
3
$begingroup$
Whether or not this is possible and realistic goal depends heavily on the problem domain and nature of your data. Could you please share some more details by using edit to add this information to the question?
$endgroup$
– Neil Slater
yesterday
add a comment |
$begingroup$
Is there a possibility of attaining the above? Can someone share with me how to go about doing it if it is?
machine-learning python
edited yesterday
Community♦
1
asked yesterday
RenaeRenae
164
New contributor
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Is there a possibility of attaining the above? Can someone share with me how to go about doing it if it is?
machine-learning python
machine-learning python
edited yesterday
Community♦
1
asked yesterday
RenaeRenae
164
New contributor
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Community♦
1
asked yesterday
RenaeRenae
164
New contributor
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
edited yesterday
Community♦
1
edited yesterday
Community♦
1
edited yesterday
Community♦
1
1
asked yesterday
RenaeRenae
164
New contributor
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
RenaeRenae
164
asked yesterday
RenaeRenae
164
164
New contributor
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Renae is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
put on hold as too broad by Mark.F, Dawny33♦ yesterday
Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
put on hold as too broad by Mark.F, Dawny33♦ yesterday
Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.
3
$begingroup$
Whether or not this is possible and realistic goal depends heavily on the problem domain and nature of your data. Could you please share some more details by using edit to add this information to the question?
$endgroup$
– Neil Slater
yesterday
add a comment |
3
$begingroup$
Whether or not this is possible and realistic goal depends heavily on the problem domain and nature of your data. Could you please share some more details by using edit to add this information to the question?
$endgroup$
– Neil Slater
yesterday
3
3
$begingroup$
Whether or not this is possible and realistic goal depends heavily on the problem domain and nature of your data. Could you please share some more details by using edit to add this information to the question?
$endgroup$
– Neil Slater
yesterday
$begingroup$
Whether or not this is possible and realistic goal depends heavily on the problem domain and nature of your data. Could you please share some more details by using edit to add this information to the question?
$endgroup$
– Neil Slater
yesterday
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
Bayes error
To answer your question, first I should explain Bayes error. Assuming we know the exact joint distribution of feature vectors ($mathbfx$) and each class ($C_k$) as
$P(mathbfx,C_k)$, we build a classifier which assigns label $k$ to each feature vector by this criteria $$mathop arg max limits_k P(left. C_k right|mathbfx)$$
It can be shown that this is the best possible classifier by calculating the expected classification error on the whole feature space. This expected classification error is called Bayes error and is the minimum achievable classification error for this feature-label space.
Training error
If you evaluate your model on the training data and calculate confusion matrix using the training samples you may achieve 100% accuracy because your model may overfit your training data. It means your training error is 0 even the Bayes error may not be.
Generalization error
If you evaluate your model on the test data and calculate confusion matrix using the test samples you can not achieve 100% accuracy because you are evaluating the generalization capability of your model and its error can not be less than Bayes error.
answered yesterday
pythinkerpythinker
8191213
$endgroup$
2
$begingroup$
This is the most realistic and general answer for the question as written. However, OP might be working on a problem where 100% generalisation accuracy is theoretically possible (i.e. Bayes error is zero). They might also have a data set where it is practical to train for this goal (enough coverage of function domain that a ML approximation can get arbitrarily close to 100% accuracy). These things are highly unlikely for many real world examples, but perhaps OP has some kind of special case.
$endgroup$
– Neil Slater
yesterday
$begingroup$
@NeilSlater I’m very proud you approved of my answer Neil. Thanks for your comments which led to further clarification.
$endgroup$
– pythinker
yesterday
add a comment |
$begingroup$
Achieving such accuracy is hard but not impossible, especially when you test your model in real life to see if the model can achieve the same accuracy or not, here are some tips that help to improve your model accuracy:
1- change the algorithm that you used to train your model, for example, if you use a traditional machine learning algorithm like SVM, try using one of the deep learning algorithms such as CNN.
2- Obtain more data, change the quality of your data, do augmentation for your data, do some pre-processing on your data, or try other pre-processing techniques if you did already.
for more see here or here or here
answered yesterday
SoKSoK
31814
$endgroup$
add a comment |
2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Bayes error
To answer your question, first I should explain Bayes error. Assuming we know the exact joint distribution of feature vectors ($mathbfx$) and each class ($C_k$) as
$P(mathbfx,C_k)$, we build a classifier which assigns label $k$ to each feature vector by this criteria $$mathop arg max limits_k P(left. C_k right|mathbfx)$$
It can be shown that this is the best possible classifier by calculating the expected classification error on the whole feature space. This expected classification error is called Bayes error and is the minimum achievable classification error for this feature-label space.
Training error
If you evaluate your model on the training data and calculate confusion matrix using the training samples you may achieve 100% accuracy because your model may overfit your training data. It means your training error is 0 even the Bayes error may not be.
Generalization error
If you evaluate your model on the test data and calculate confusion matrix using the test samples you can not achieve 100% accuracy because you are evaluating the generalization capability of your model and its error can not be less than Bayes error.
answered yesterday
pythinkerpythinker
8191213
$endgroup$
2
$begingroup$
This is the most realistic and general answer for the question as written. However, OP might be working on a problem where 100% generalisation accuracy is theoretically possible (i.e. Bayes error is zero). They might also have a data set where it is practical to train for this goal (enough coverage of function domain that a ML approximation can get arbitrarily close to 100% accuracy). These things are highly unlikely for many real world examples, but perhaps OP has some kind of special case.
$endgroup$
– Neil Slater
yesterday
$begingroup$
@NeilSlater I’m very proud you approved of my answer Neil. Thanks for your comments which led to further clarification.
$endgroup$
– pythinker
yesterday
add a comment |
$begingroup$
Bayes error
To answer your question, first I should explain Bayes error. Assuming we know the exact joint distribution of feature vectors ($mathbfx$) and each class ($C_k$) as
$P(mathbfx,C_k)$, we build a classifier which assigns label $k$ to each feature vector by this criteria $$mathop arg max limits_k P(left. C_k right|mathbfx)$$
It can be shown that this is the best possible classifier by calculating the expected classification error on the whole feature space. This expected classification error is called Bayes error and is the minimum achievable classification error for this feature-label space.
Training error
If you evaluate your model on the training data and calculate confusion matrix using the training samples you may achieve 100% accuracy because your model may overfit your training data. It means your training error is 0 even the Bayes error may not be.
Generalization error
If you evaluate your model on the test data and calculate confusion matrix using the test samples you can not achieve 100% accuracy because you are evaluating the generalization capability of your model and its error can not be less than Bayes error.
answered yesterday
pythinkerpythinker
8191213
$endgroup$
2
$begingroup$
This is the most realistic and general answer for the question as written. However, OP might be working on a problem where 100% generalisation accuracy is theoretically possible (i.e. Bayes error is zero). They might also have a data set where it is practical to train for this goal (enough coverage of function domain that a ML approximation can get arbitrarily close to 100% accuracy). These things are highly unlikely for many real world examples, but perhaps OP has some kind of special case.
$endgroup$
– Neil Slater
yesterday
$begingroup$
@NeilSlater I’m very proud you approved of my answer Neil. Thanks for your comments which led to further clarification.
$endgroup$
– pythinker
yesterday
add a comment |
$begingroup$
Bayes error
To answer your question, first I should explain Bayes error. Assuming we know the exact joint distribution of feature vectors ($mathbfx$) and each class ($C_k$) as
$P(mathbfx,C_k)$, we build a classifier which assigns label $k$ to each feature vector by this criteria $$mathop arg max limits_k P(left. C_k right|mathbfx)$$
It can be shown that this is the best possible classifier by calculating the expected classification error on the whole feature space. This expected classification error is called Bayes error and is the minimum achievable classification error for this feature-label space.
Training error
If you evaluate your model on the training data and calculate confusion matrix using the training samples you may achieve 100% accuracy because your model may overfit your training data. It means your training error is 0 even the Bayes error may not be.
Generalization error
If you evaluate your model on the test data and calculate confusion matrix using the test samples you can not achieve 100% accuracy because you are evaluating the generalization capability of your model and its error can not be less than Bayes error.
answered yesterday
pythinkerpythinker
8191213
$endgroup$
Bayes error
To answer your question, first I should explain Bayes error. Assuming we know the exact joint distribution of feature vectors ($mathbfx$) and each class ($C_k$) as
$P(mathbfx,C_k)$, we build a classifier which assigns label $k$ to each feature vector by this criteria $$mathop arg max limits_k P(left. C_k right|mathbfx)$$
It can be shown that this is the best possible classifier by calculating the expected classification error on the whole feature space. This expected classification error is called Bayes error and is the minimum achievable classification error for this feature-label space.
Training error
If you evaluate your model on the training data and calculate confusion matrix using the training samples you may achieve 100% accuracy because your model may overfit your training data. It means your training error is 0 even the Bayes error may not be.
Generalization error
If you evaluate your model on the test data and calculate confusion matrix using the test samples you can not achieve 100% accuracy because you are evaluating the generalization capability of your model and its error can not be less than Bayes error.
answered yesterday
pythinkerpythinker
8191213
answered yesterday
pythinkerpythinker
8191213
answered yesterday
pythinkerpythinker
8191213
answered yesterday
pythinkerpythinker
8191213
8191213
2
$begingroup$
This is the most realistic and general answer for the question as written. However, OP might be working on a problem where 100% generalisation accuracy is theoretically possible (i.e. Bayes error is zero). They might also have a data set where it is practical to train for this goal (enough coverage of function domain that a ML approximation can get arbitrarily close to 100% accuracy). These things are highly unlikely for many real world examples, but perhaps OP has some kind of special case.
$endgroup$
– Neil Slater
yesterday
$begingroup$
@NeilSlater I’m very proud you approved of my answer Neil. Thanks for your comments which led to further clarification.
$endgroup$
– pythinker
yesterday
add a comment |
2
$begingroup$
This is the most realistic and general answer for the question as written. However, OP might be working on a problem where 100% generalisation accuracy is theoretically possible (i.e. Bayes error is zero). They might also have a data set where it is practical to train for this goal (enough coverage of function domain that a ML approximation can get arbitrarily close to 100% accuracy). These things are highly unlikely for many real world examples, but perhaps OP has some kind of special case.
$endgroup$
– Neil Slater
yesterday
$begingroup$
@NeilSlater I’m very proud you approved of my answer Neil. Thanks for your comments which led to further clarification.
$endgroup$
– pythinker
yesterday
2
2
$begingroup$
This is the most realistic and general answer for the question as written. However, OP might be working on a problem where 100% generalisation accuracy is theoretically possible (i.e. Bayes error is zero). They might also have a data set where it is practical to train for this goal (enough coverage of function domain that a ML approximation can get arbitrarily close to 100% accuracy). These things are highly unlikely for many real world examples, but perhaps OP has some kind of special case.
$endgroup$
– Neil Slater
yesterday
$begingroup$
This is the most realistic and general answer for the question as written. However, OP might be working on a problem where 100% generalisation accuracy is theoretically possible (i.e. Bayes error is zero). They might also have a data set where it is practical to train for this goal (enough coverage of function domain that a ML approximation can get arbitrarily close to 100% accuracy). These things are highly unlikely for many real world examples, but perhaps OP has some kind of special case.
$endgroup$
– Neil Slater
yesterday
$begingroup$
@NeilSlater I’m very proud you approved of my answer Neil. Thanks for your comments which led to further clarification.
$endgroup$
– pythinker
yesterday
$begingroup$
@NeilSlater I’m very proud you approved of my answer Neil. Thanks for your comments which led to further clarification.
$endgroup$
– pythinker
yesterday
add a comment |
$begingroup$
Achieving such accuracy is hard but not impossible, especially when you test your model in real life to see if the model can achieve the same accuracy or not, here are some tips that help to improve your model accuracy:
1- change the algorithm that you used to train your model, for example, if you use a traditional machine learning algorithm like SVM, try using one of the deep learning algorithms such as CNN.
2- Obtain more data, change the quality of your data, do augmentation for your data, do some pre-processing on your data, or try other pre-processing techniques if you did already.
for more see here or here or here
answered yesterday
SoKSoK
31814
$endgroup$
add a comment |
$begingroup$
Achieving such accuracy is hard but not impossible, especially when you test your model in real life to see if the model can achieve the same accuracy or not, here are some tips that help to improve your model accuracy:
1- change the algorithm that you used to train your model, for example, if you use a traditional machine learning algorithm like SVM, try using one of the deep learning algorithms such as CNN.
2- Obtain more data, change the quality of your data, do augmentation for your data, do some pre-processing on your data, or try other pre-processing techniques if you did already.
for more see here or here or here
answered yesterday
SoKSoK
31814
$endgroup$
add a comment |
$begingroup$
Achieving such accuracy is hard but not impossible, especially when you test your model in real life to see if the model can achieve the same accuracy or not, here are some tips that help to improve your model accuracy:
1- change the algorithm that you used to train your model, for example, if you use a traditional machine learning algorithm like SVM, try using one of the deep learning algorithms such as CNN.
2- Obtain more data, change the quality of your data, do augmentation for your data, do some pre-processing on your data, or try other pre-processing techniques if you did already.
for more see here or here or here
answered yesterday
SoKSoK
31814
$endgroup$
Achieving such accuracy is hard but not impossible, especially when you test your model in real life to see if the model can achieve the same accuracy or not, here are some tips that help to improve your model accuracy:
1- change the algorithm that you used to train your model, for example, if you use a traditional machine learning algorithm like SVM, try using one of the deep learning algorithms such as CNN.
2- Obtain more data, change the quality of your data, do augmentation for your data, do some pre-processing on your data, or try other pre-processing techniques if you did already.
for more see here or here or here
answered yesterday
SoKSoK
31814
answered yesterday
SoKSoK
31814
answered yesterday
SoKSoK
31814
answered yesterday
SoKSoK
31814
31814
add a comment |
add a comment |
-machine-learning, python
3
$begingroup$
Whether or not this is possible and realistic goal depends heavily on the problem domain and nature of your data. Could you please share some more details by using edit to add this information to the question?
$endgroup$
– Neil Slater
yesterday