Accuracy after selftraining didn't change The Next CEO of Stack Overflow2019 Community Moderator ElectionHow to increase accuracy of classifiers?Improving Naive Bayes accuracy for text classificationWhat is a reasonable way to compare the improvement in accuracy?Accuracy value constant even after different runsIs this a good classified model based confusion matrix and classification report?Max 75% accuracy! help!Coursera ML - Does the choice of optimization algorithm affect the accuracy of multiclass logistic regression?I got 100% accuracy on my test set,is there something wrong?Validation accuracy is always close to training accuracyHow to get probability of classification
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Accuracy after selftraining didn't change
The Next CEO of Stack Overflow2019 Community Moderator ElectionHow to increase accuracy of classifiers?Improving Naive Bayes accuracy for text classificationWhat is a reasonable way to compare the improvement in accuracy?Accuracy value constant even after different runsIs this a good classified model based confusion matrix and classification report?Max 75% accuracy! help!Coursera ML - Does the choice of optimization algorithm affect the accuracy of multiclass logistic regression?I got 100% accuracy on my test set,is there something wrong?Validation accuracy is always close to training accuracyHow to get probability of classification
$begingroup$
I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would like to ask if it is normal, that after retrainig model with these 10 000 unlabeled samples, accuracy didn't chaned as well as confusion matrix has same values? I expected some changes (better or worse prediction). Thank you in advance.
accuracy semi-supervised-learning
asked Mar 23 at 13:10
SMI9SMI9
63
$endgroup$
add a comment |
$begingroup$
I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would like to ask if it is normal, that after retrainig model with these 10 000 unlabeled samples, accuracy didn't chaned as well as confusion matrix has same values? I expected some changes (better or worse prediction). Thank you in advance.
accuracy semi-supervised-learning
asked Mar 23 at 13:10
SMI9SMI9
63
$endgroup$
add a comment |
$begingroup$
I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would like to ask if it is normal, that after retrainig model with these 10 000 unlabeled samples, accuracy didn't chaned as well as confusion matrix has same values? I expected some changes (better or worse prediction). Thank you in advance.
accuracy semi-supervised-learning
asked Mar 23 at 13:10
SMI9SMI9
63
$endgroup$
I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would like to ask if it is normal, that after retrainig model with these 10 000 unlabeled samples, accuracy didn't chaned as well as confusion matrix has same values? I expected some changes (better or worse prediction). Thank you in advance.
accuracy semi-supervised-learning
accuracy semi-supervised-learning
asked Mar 23 at 13:10
SMI9SMI9
63
asked Mar 23 at 13:10
SMI9SMI9
63
asked Mar 23 at 13:10
SMI9SMI9
63
asked Mar 23 at 13:10
SMI9SMI9
63
asked Mar 23 at 13:10
SMI9SMI9
63
63
add a comment |
add a comment |
1 Answer
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$begingroup$
Well, that is a bit of a turn down but: your model has limitations.
If the 50.000 data forms a complete set for your problem that means that more data won't be needed or helpful.
What do I mean by complete set is: there are enough samples to form a full rank correlation matrix in your feature space. So from your samples you can get a set that can generate all other samples in your feature space by linear combination.
Also, while your data might represent everything a decision three needs to know for classificating your data in the generated feature space, there may be other feature spaces that benefit from the extra data (such as deeper trees or other models)
You might try helping you decision tree by providing a few normalizations for data and feature engineering
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
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1 Answer
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$begingroup$
Well, that is a bit of a turn down but: your model has limitations.
If the 50.000 data forms a complete set for your problem that means that more data won't be needed or helpful.
What do I mean by complete set is: there are enough samples to form a full rank correlation matrix in your feature space. So from your samples you can get a set that can generate all other samples in your feature space by linear combination.
Also, while your data might represent everything a decision three needs to know for classificating your data in the generated feature space, there may be other feature spaces that benefit from the extra data (such as deeper trees or other models)
You might try helping you decision tree by providing a few normalizations for data and feature engineering
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Well, that is a bit of a turn down but: your model has limitations.
If the 50.000 data forms a complete set for your problem that means that more data won't be needed or helpful.
What do I mean by complete set is: there are enough samples to form a full rank correlation matrix in your feature space. So from your samples you can get a set that can generate all other samples in your feature space by linear combination.
Also, while your data might represent everything a decision three needs to know for classificating your data in the generated feature space, there may be other feature spaces that benefit from the extra data (such as deeper trees or other models)
You might try helping you decision tree by providing a few normalizations for data and feature engineering
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
Well, that is a bit of a turn down but: your model has limitations.
If the 50.000 data forms a complete set for your problem that means that more data won't be needed or helpful.
What do I mean by complete set is: there are enough samples to form a full rank correlation matrix in your feature space. So from your samples you can get a set that can generate all other samples in your feature space by linear combination.
Also, while your data might represent everything a decision three needs to know for classificating your data in the generated feature space, there may be other feature spaces that benefit from the extra data (such as deeper trees or other models)
You might try helping you decision tree by providing a few normalizations for data and feature engineering
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Well, that is a bit of a turn down but: your model has limitations.
If the 50.000 data forms a complete set for your problem that means that more data won't be needed or helpful.
What do I mean by complete set is: there are enough samples to form a full rank correlation matrix in your feature space. So from your samples you can get a set that can generate all other samples in your feature space by linear combination.
Also, while your data might represent everything a decision three needs to know for classificating your data in the generated feature space, there may be other feature spaces that benefit from the extra data (such as deeper trees or other models)
You might try helping you decision tree by providing a few normalizations for data and feature engineering
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
answered Mar 24 at 4:17
Pedro Henrique MonfortePedro Henrique Monforte
885
885
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Pedro Henrique Monforte is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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