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R h2o.deeplearning obtaining probabilities with classification mode
Probability and Neural NetworksA simple explanation of Naive Bayes ClassificationNon-linear classification vs regression with FFANNNeural Networks Regression : scaling the outputs or using a linear layer?Convert classification to regression in tensorflowH2o Grid-Search for Quantile Regression/Prediction IntervalPredict classes or class probabilities?Neural Network under fitting - breast cancer datasetTraining RMSE higher than Validation RMSE in H2OWhat to do when the training data has classification labels but the required task is probabillities?
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I am using h2o.deeplearning to train a neural network on a classification task.
What I have
Y ~ x1 + x2... where all x variables are continuous and Y is binary.
What I want
To be able to train a deeplearning object to predict the probability of a given row of being true or false. That is, a predicted(Y) restricted to between 0 and 1.
What I've tried
When Y is inputted as a numeric (i.e. 0 or 1), h2o deeplearning automatically treats it as a regression problem. This is fine, except the final layer of the NN is linear, not tanh, and the predicted values can be greater than 1 or less than 0. I've not been able to find a way to get the final layer to be a tanh.
When Y is inputted as categorical (i.e. TRUE or FALSE), h2o deeplearning automatically treats it as a classification problem. Instead of giving me the desired probability of Y being 1 or 0, it gives me its best guess of what Y is.
Is there a way around this? A trick, tweak or an overlooked parameter? I have noticed in the h2o.deeplearning documentation a 'distribution' parameter, but no further information on what that's for. My best guess is that it is some kind of link function in the same vein as GLM, but I'm not sure.
r regression classification h2o
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
add a comment |
I am using h2o.deeplearning to train a neural network on a classification task.
What I have
Y ~ x1 + x2... where all x variables are continuous and Y is binary.
What I want
To be able to train a deeplearning object to predict the probability of a given row of being true or false. That is, a predicted(Y) restricted to between 0 and 1.
What I've tried
When Y is inputted as a numeric (i.e. 0 or 1), h2o deeplearning automatically treats it as a regression problem. This is fine, except the final layer of the NN is linear, not tanh, and the predicted values can be greater than 1 or less than 0. I've not been able to find a way to get the final layer to be a tanh.
When Y is inputted as categorical (i.e. TRUE or FALSE), h2o deeplearning automatically treats it as a classification problem. Instead of giving me the desired probability of Y being 1 or 0, it gives me its best guess of what Y is.
Is there a way around this? A trick, tweak or an overlooked parameter? I have noticed in the h2o.deeplearning documentation a 'distribution' parameter, but no further information on what that's for. My best guess is that it is some kind of link function in the same vein as GLM, but I'm not sure.
r regression classification h2o
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
1
Well, you haven't offered any code. You haven't offered any data. And you haven't offered any links to sites whose documentation you have already researched, so we have no idea how much effort you put into this. I wonder if this might be closer to the topics handled at the beta data science section of stackexchange? (And don't blame me, they aren't my votes, so these are just guesses as to motivations.)
– 42-
Mar 26 at 0:23
add a comment |
I am using h2o.deeplearning to train a neural network on a classification task.
What I have
Y ~ x1 + x2... where all x variables are continuous and Y is binary.
What I want
To be able to train a deeplearning object to predict the probability of a given row of being true or false. That is, a predicted(Y) restricted to between 0 and 1.
What I've tried
When Y is inputted as a numeric (i.e. 0 or 1), h2o deeplearning automatically treats it as a regression problem. This is fine, except the final layer of the NN is linear, not tanh, and the predicted values can be greater than 1 or less than 0. I've not been able to find a way to get the final layer to be a tanh.
When Y is inputted as categorical (i.e. TRUE or FALSE), h2o deeplearning automatically treats it as a classification problem. Instead of giving me the desired probability of Y being 1 or 0, it gives me its best guess of what Y is.
Is there a way around this? A trick, tweak or an overlooked parameter? I have noticed in the h2o.deeplearning documentation a 'distribution' parameter, but no further information on what that's for. My best guess is that it is some kind of link function in the same vein as GLM, but I'm not sure.
r regression classification h2o
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
I am using h2o.deeplearning to train a neural network on a classification task.
What I have
Y ~ x1 + x2... where all x variables are continuous and Y is binary.
What I want
To be able to train a deeplearning object to predict the probability of a given row of being true or false. That is, a predicted(Y) restricted to between 0 and 1.
What I've tried
When Y is inputted as a numeric (i.e. 0 or 1), h2o deeplearning automatically treats it as a regression problem. This is fine, except the final layer of the NN is linear, not tanh, and the predicted values can be greater than 1 or less than 0. I've not been able to find a way to get the final layer to be a tanh.
When Y is inputted as categorical (i.e. TRUE or FALSE), h2o deeplearning automatically treats it as a classification problem. Instead of giving me the desired probability of Y being 1 or 0, it gives me its best guess of what Y is.
Is there a way around this? A trick, tweak or an overlooked parameter? I have noticed in the h2o.deeplearning documentation a 'distribution' parameter, but no further information on what that's for. My best guess is that it is some kind of link function in the same vein as GLM, but I'm not sure.
r regression classification h2o
r regression classification h2o
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
asked Mar 25 at 21:42
IngolifsIngolifs
1216 bronze badges
1216 bronze badges
1
Well, you haven't offered any code. You haven't offered any data. And you haven't offered any links to sites whose documentation you have already researched, so we have no idea how much effort you put into this. I wonder if this might be closer to the topics handled at the beta data science section of stackexchange? (And don't blame me, they aren't my votes, so these are just guesses as to motivations.)
– 42-
Mar 26 at 0:23
add a comment |
1
Well, you haven't offered any code. You haven't offered any data. And you haven't offered any links to sites whose documentation you have already researched, so we have no idea how much effort you put into this. I wonder if this might be closer to the topics handled at the beta data science section of stackexchange? (And don't blame me, they aren't my votes, so these are just guesses as to motivations.)
– 42-
Mar 26 at 0:23
1
1
Well, you haven't offered any code. You haven't offered any data. And you haven't offered any links to sites whose documentation you have already researched, so we have no idea how much effort you put into this. I wonder if this might be closer to the topics handled at the beta data science section of stackexchange? (And don't blame me, they aren't my votes, so these are just guesses as to motivations.)
– 42-
Mar 26 at 0:23
Well, you haven't offered any code. You haven't offered any data. And you haven't offered any links to sites whose documentation you have already researched, so we have no idea how much effort you put into this. I wonder if this might be closer to the topics handled at the beta data science section of stackexchange? (And don't blame me, they aren't my votes, so these are just guesses as to motivations.)
– 42-
Mar 26 at 0:23
add a comment |
1 Answer
1
active
oldest
votes
If you treat the problem as a binary classification problem then you not only get the “prediction” of 0 or 1, but also the p0 and p1 probabilities that add up to 1. These are the probabilies that the predicted value is the negative and positive class, respectively.
Then just use p1 directly.
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
add a comment |
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If you treat the problem as a binary classification problem then you not only get the “prediction” of 0 or 1, but also the p0 and p1 probabilities that add up to 1. These are the probabilies that the predicted value is the negative and positive class, respectively.
Then just use p1 directly.
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
add a comment |
If you treat the problem as a binary classification problem then you not only get the “prediction” of 0 or 1, but also the p0 and p1 probabilities that add up to 1. These are the probabilies that the predicted value is the negative and positive class, respectively.
Then just use p1 directly.
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
add a comment |
If you treat the problem as a binary classification problem then you not only get the “prediction” of 0 or 1, but also the p0 and p1 probabilities that add up to 1. These are the probabilies that the predicted value is the negative and positive class, respectively.
Then just use p1 directly.
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
If you treat the problem as a binary classification problem then you not only get the “prediction” of 0 or 1, but also the p0 and p1 probabilities that add up to 1. These are the probabilies that the predicted value is the negative and positive class, respectively.
Then just use p1 directly.
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
answered Mar 26 at 14:35
TomKraljevicTomKraljevic
2,6457 silver badges13 bronze badges
2,6457 silver badges13 bronze badges
add a comment |
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Well, you haven't offered any code. You haven't offered any data. And you haven't offered any links to sites whose documentation you have already researched, so we have no idea how much effort you put into this. I wonder if this might be closer to the topics handled at the beta data science section of stackexchange? (And don't blame me, they aren't my votes, so these are just guesses as to motivations.)
– 42-
Mar 26 at 0:23