Machine Learning Dataset: Easy enough for fully connected, but not easy enough for logistic regression
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I was wondering if someone could direct me to a dataset for a classification task with the following conditions:
- Multinomial logistic regression alone does not learn a good classifier
- A series of fully connected layers is able to learn a good classifier
- The task is not MNIST
Thank you
machine-learning dataset logistic-regression
asked yesterday
msmmsm
1061
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I was wondering if someone could direct me to a dataset for a classification task with the following conditions:
- Multinomial logistic regression alone does not learn a good classifier
- A series of fully connected layers is able to learn a good classifier
- The task is not MNIST
Thank you
machine-learning dataset logistic-regression
asked yesterday
msmmsm
1061
New contributor
msm 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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1
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There are lots of datasets you can try but I would simply suggest XOR problem which logistic regression can not perform well on but fully connected layers can achieve a good performance.
$endgroup$
– pythinker
yesterday
$begingroup$
Multinomial logistic can learn almost everything. The point is that you yourself have to find the correct polynomials which is not easy in most of the cases.
$endgroup$
– Vaalizaadeh
yesterday
add a comment |
$begingroup$
I was wondering if someone could direct me to a dataset for a classification task with the following conditions:
- Multinomial logistic regression alone does not learn a good classifier
- A series of fully connected layers is able to learn a good classifier
- The task is not MNIST
Thank you
machine-learning dataset logistic-regression
asked yesterday
msmmsm
1061
New contributor
msm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I was wondering if someone could direct me to a dataset for a classification task with the following conditions:
- Multinomial logistic regression alone does not learn a good classifier
- A series of fully connected layers is able to learn a good classifier
- The task is not MNIST
Thank you
machine-learning dataset logistic-regression
machine-learning dataset logistic-regression
asked yesterday
msmmsm
1061
New contributor
msm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
msmmsm
1061
New contributor
msm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
msmmsm
1061
New contributor
msm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
msmmsm
1061
asked yesterday
msmmsm
1061
1061
New contributor
msm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
msm 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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1
$begingroup$
There are lots of datasets you can try but I would simply suggest XOR problem which logistic regression can not perform well on but fully connected layers can achieve a good performance.
$endgroup$
– pythinker
yesterday
$begingroup$
Multinomial logistic can learn almost everything. The point is that you yourself have to find the correct polynomials which is not easy in most of the cases.
$endgroup$
– Vaalizaadeh
yesterday
add a comment |
1
$begingroup$
There are lots of datasets you can try but I would simply suggest XOR problem which logistic regression can not perform well on but fully connected layers can achieve a good performance.
$endgroup$
– pythinker
yesterday
$begingroup$
Multinomial logistic can learn almost everything. The point is that you yourself have to find the correct polynomials which is not easy in most of the cases.
$endgroup$
– Vaalizaadeh
yesterday
1
1
$begingroup$
There are lots of datasets you can try but I would simply suggest XOR problem which logistic regression can not perform well on but fully connected layers can achieve a good performance.
$endgroup$
– pythinker
yesterday
$begingroup$
There are lots of datasets you can try but I would simply suggest XOR problem which logistic regression can not perform well on but fully connected layers can achieve a good performance.
$endgroup$
– pythinker
yesterday
$begingroup$
Multinomial logistic can learn almost everything. The point is that you yourself have to find the correct polynomials which is not easy in most of the cases.
$endgroup$
– Vaalizaadeh
yesterday
$begingroup$
Multinomial logistic can learn almost everything. The point is that you yourself have to find the correct polynomials which is not easy in most of the cases.
$endgroup$
– Vaalizaadeh
yesterday
add a comment |
1 Answer
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Here's a one-dimensional problem that should be impossible for logistic regression:
- Generate x uniformly on [0, 10]
- Let y = sin(2 * pi * x)
If you want a classification problem define y_disc as:
- 0 if y > 1/sqrt(2)
- 1 if 1/sqrt(2) > y > -1/sqrt(2)
- 2 if y < -1/sqrt(2).
This is non-linear, so logistic regression should do poorly. Further, if you decide to put higher powers of x into your logistic regression classifier, I think you'll need a lot of powers to accurately represent the Taylor series near 10.
If fact, you could try adding a small noise term to y before discretizing - I suspect that will result in your logistic regressor overfitting long before it's able to accurately approximate the behavior near x=10.
answered yesterday
Harry BravinerHarry Braviner
1111
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$begingroup$
Here's a one-dimensional problem that should be impossible for logistic regression:
- Generate x uniformly on [0, 10]
- Let y = sin(2 * pi * x)
If you want a classification problem define y_disc as:
- 0 if y > 1/sqrt(2)
- 1 if 1/sqrt(2) > y > -1/sqrt(2)
- 2 if y < -1/sqrt(2).
This is non-linear, so logistic regression should do poorly. Further, if you decide to put higher powers of x into your logistic regression classifier, I think you'll need a lot of powers to accurately represent the Taylor series near 10.
If fact, you could try adding a small noise term to y before discretizing - I suspect that will result in your logistic regressor overfitting long before it's able to accurately approximate the behavior near x=10.
answered yesterday
Harry BravinerHarry Braviner
1111
New contributor
Harry Braviner 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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add a comment |
$begingroup$
Here's a one-dimensional problem that should be impossible for logistic regression:
- Generate x uniformly on [0, 10]
- Let y = sin(2 * pi * x)
If you want a classification problem define y_disc as:
- 0 if y > 1/sqrt(2)
- 1 if 1/sqrt(2) > y > -1/sqrt(2)
- 2 if y < -1/sqrt(2).
This is non-linear, so logistic regression should do poorly. Further, if you decide to put higher powers of x into your logistic regression classifier, I think you'll need a lot of powers to accurately represent the Taylor series near 10.
If fact, you could try adding a small noise term to y before discretizing - I suspect that will result in your logistic regressor overfitting long before it's able to accurately approximate the behavior near x=10.
answered yesterday
Harry BravinerHarry Braviner
1111
New contributor
Harry Braviner 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 |
$begingroup$
Here's a one-dimensional problem that should be impossible for logistic regression:
- Generate x uniformly on [0, 10]
- Let y = sin(2 * pi * x)
If you want a classification problem define y_disc as:
- 0 if y > 1/sqrt(2)
- 1 if 1/sqrt(2) > y > -1/sqrt(2)
- 2 if y < -1/sqrt(2).
This is non-linear, so logistic regression should do poorly. Further, if you decide to put higher powers of x into your logistic regression classifier, I think you'll need a lot of powers to accurately represent the Taylor series near 10.
If fact, you could try adding a small noise term to y before discretizing - I suspect that will result in your logistic regressor overfitting long before it's able to accurately approximate the behavior near x=10.
answered yesterday
Harry BravinerHarry Braviner
1111
New contributor
Harry Braviner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Here's a one-dimensional problem that should be impossible for logistic regression:
- Generate x uniformly on [0, 10]
- Let y = sin(2 * pi * x)
If you want a classification problem define y_disc as:
- 0 if y > 1/sqrt(2)
- 1 if 1/sqrt(2) > y > -1/sqrt(2)
- 2 if y < -1/sqrt(2).
This is non-linear, so logistic regression should do poorly. Further, if you decide to put higher powers of x into your logistic regression classifier, I think you'll need a lot of powers to accurately represent the Taylor series near 10.
If fact, you could try adding a small noise term to y before discretizing - I suspect that will result in your logistic regressor overfitting long before it's able to accurately approximate the behavior near x=10.
answered yesterday
Harry BravinerHarry Braviner
1111
New contributor
Harry Braviner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered yesterday
Harry BravinerHarry Braviner
1111
New contributor
Harry Braviner is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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answered yesterday
Harry BravinerHarry Braviner
1111
answered yesterday
Harry BravinerHarry Braviner
1111
1111
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1
$begingroup$
There are lots of datasets you can try but I would simply suggest XOR problem which logistic regression can not perform well on but fully connected layers can achieve a good performance.
$endgroup$
– pythinker
yesterday
$begingroup$
Multinomial logistic can learn almost everything. The point is that you yourself have to find the correct polynomials which is not easy in most of the cases.
$endgroup$
– Vaalizaadeh
yesterday