In handwritten digit recognition problem using logistic regression, what changes needed to add another class...
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In handwritten digit recognition problem using logistic regression, normal implementation would forcibly classify even a picture of dog or cat as a digit. To eliminate this, what changes are needed to add another class i.e. "Not a Digit" to already existing 10 classes (0 to 9) ?
scikit-learn logistic-regression multiclass-classification image-recognition multilabel-classification
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
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$begingroup$
In handwritten digit recognition problem using logistic regression, normal implementation would forcibly classify even a picture of dog or cat as a digit. To eliminate this, what changes are needed to add another class i.e. "Not a Digit" to already existing 10 classes (0 to 9) ?
scikit-learn logistic-regression multiclass-classification image-recognition multilabel-classification
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
$endgroup$
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
$begingroup$
In handwritten digit recognition problem using logistic regression, normal implementation would forcibly classify even a picture of dog or cat as a digit. To eliminate this, what changes are needed to add another class i.e. "Not a Digit" to already existing 10 classes (0 to 9) ?
scikit-learn logistic-regression multiclass-classification image-recognition multilabel-classification
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
$endgroup$
In handwritten digit recognition problem using logistic regression, normal implementation would forcibly classify even a picture of dog or cat as a digit. To eliminate this, what changes are needed to add another class i.e. "Not a Digit" to already existing 10 classes (0 to 9) ?
scikit-learn logistic-regression multiclass-classification image-recognition multilabel-classification
scikit-learn logistic-regression multiclass-classification image-recognition multilabel-classification
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
asked Nov 16 '18 at 6:20
Deependra Singh Deependra Singh
61
61
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 15 hours ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
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Logistic regression is normally used to perform binary classification, which answers a yes or no question, e.g.:
- Is this an 8 or not?
- Will it rain today or not?
Perhaps I have misunderstood your explanation, but it sounds like you are trying to perform multi-class classification, i.e. to classify an image as one of a certain number of options. So a single image must contain (be classified as) a number from the list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].
Usually, to add an extra class to a model that already does this, you would need to adjust the final output of the model to predict a one-hot vector that is simply one element longer!
In the specific case of the Scikit-Learn LogosticRegression class, it seems as though you don't need to specify anything - the class will automatically use a multinomial model and relevant optimiser as soon as it sees that data is not binary (i.e. a yes-no model as explained above).
Have a look at this official tutorial, which should a multinomial model trained on a dataset with a target variable that has a total of 20 classes. The number of features (n_classes is equal to 20) is not passed to the model at all, it is inferred.
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
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1 Answer
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$begingroup$
Logistic regression is normally used to perform binary classification, which answers a yes or no question, e.g.:
- Is this an 8 or not?
- Will it rain today or not?
Perhaps I have misunderstood your explanation, but it sounds like you are trying to perform multi-class classification, i.e. to classify an image as one of a certain number of options. So a single image must contain (be classified as) a number from the list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].
Usually, to add an extra class to a model that already does this, you would need to adjust the final output of the model to predict a one-hot vector that is simply one element longer!
In the specific case of the Scikit-Learn LogosticRegression class, it seems as though you don't need to specify anything - the class will automatically use a multinomial model and relevant optimiser as soon as it sees that data is not binary (i.e. a yes-no model as explained above).
Have a look at this official tutorial, which should a multinomial model trained on a dataset with a target variable that has a total of 20 classes. The number of features (n_classes is equal to 20) is not passed to the model at all, it is inferred.
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
$endgroup$
add a comment |
$begingroup$
Logistic regression is normally used to perform binary classification, which answers a yes or no question, e.g.:
- Is this an 8 or not?
- Will it rain today or not?
Perhaps I have misunderstood your explanation, but it sounds like you are trying to perform multi-class classification, i.e. to classify an image as one of a certain number of options. So a single image must contain (be classified as) a number from the list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].
Usually, to add an extra class to a model that already does this, you would need to adjust the final output of the model to predict a one-hot vector that is simply one element longer!
In the specific case of the Scikit-Learn LogosticRegression class, it seems as though you don't need to specify anything - the class will automatically use a multinomial model and relevant optimiser as soon as it sees that data is not binary (i.e. a yes-no model as explained above).
Have a look at this official tutorial, which should a multinomial model trained on a dataset with a target variable that has a total of 20 classes. The number of features (n_classes is equal to 20) is not passed to the model at all, it is inferred.
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
$endgroup$
add a comment |
$begingroup$
Logistic regression is normally used to perform binary classification, which answers a yes or no question, e.g.:
- Is this an 8 or not?
- Will it rain today or not?
Perhaps I have misunderstood your explanation, but it sounds like you are trying to perform multi-class classification, i.e. to classify an image as one of a certain number of options. So a single image must contain (be classified as) a number from the list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].
Usually, to add an extra class to a model that already does this, you would need to adjust the final output of the model to predict a one-hot vector that is simply one element longer!
In the specific case of the Scikit-Learn LogosticRegression class, it seems as though you don't need to specify anything - the class will automatically use a multinomial model and relevant optimiser as soon as it sees that data is not binary (i.e. a yes-no model as explained above).
Have a look at this official tutorial, which should a multinomial model trained on a dataset with a target variable that has a total of 20 classes. The number of features (n_classes is equal to 20) is not passed to the model at all, it is inferred.
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
$endgroup$
Logistic regression is normally used to perform binary classification, which answers a yes or no question, e.g.:
- Is this an 8 or not?
- Will it rain today or not?
Perhaps I have misunderstood your explanation, but it sounds like you are trying to perform multi-class classification, i.e. to classify an image as one of a certain number of options. So a single image must contain (be classified as) a number from the list: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9].
Usually, to add an extra class to a model that already does this, you would need to adjust the final output of the model to predict a one-hot vector that is simply one element longer!
In the specific case of the Scikit-Learn LogosticRegression class, it seems as though you don't need to specify anything - the class will automatically use a multinomial model and relevant optimiser as soon as it sees that data is not binary (i.e. a yes-no model as explained above).
Have a look at this official tutorial, which should a multinomial model trained on a dataset with a target variable that has a total of 20 classes. The number of features (n_classes is equal to 20) is not passed to the model at all, it is inferred.
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
answered Nov 16 '18 at 8:57
n1k31t4n1k31t4
6,5312421
6,5312421
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