intercept correction in deep learning The 2019 Stack Overflow Developer Survey Results Are In Announcing the arrival of Valued Associate #679: Cesar Manara Planned maintenance scheduled April 17/18, 2019 at 11:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsWhy real-world output of my classifier has similar label ratio to training data?Deep learning basicsPolicy network AlphaGo and transferring to other domainsMachine Learning vs Deep LearningBinary Classifier making only one predictionCreating an easy but not trivial datasetSeveral fundamental questions about CNNHow to add non-image features along side images as the input of CNNsRunning two stage classification to predict relatively rare event?Poor performance of SVM after training for rare eventsconvLSTM : how to structure input data
Can each chord in a progression create its own key?
How to politely respond to generic emails requesting a PhD/job in my lab? Without wasting too much time
What information about me do stores get via my credit card?
Word for: a synonym with a positive connotation?
Using `min_active_rowversion` for global temporary tables
Nested ellipses in tikzpicture: Chomsky hierarchy
Word to describe a time interval
Why can't devices on different VLANs, but on the same subnet, communicate?
Single author papers against my advisor's will?
Windows 10: How to Lock (not sleep) laptop on lid close?
Am I ethically obligated to go into work on an off day if the reason is sudden?
How to read αἱμύλιος or when to aspirate
Categorical vs continuous feature selection/engineering
Button changing its text & action. Good or terrible?
How to substitute curly brackets with round brackets in a grid of list
Didn't get enough time to take a Coding Test - what to do now?
What can I do if neighbor is blocking my solar panels intentionally?
What happens to a Warlock's expended Spell Slots when they gain a Level?
How to handle characters who are more educated than the author?
Is every episode of "Where are my Pants?" identical?
Is an up-to-date browser secure on an out-of-date OS?
Why doesn't shell automatically fix "useless use of cat"?
Are spiders unable to hurt humans, especially very small spiders?
Pretty sure I'm over complicating my loops but unsure how to simplify
intercept correction in deep learning
The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 11:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsWhy real-world output of my classifier has similar label ratio to training data?Deep learning basicsPolicy network AlphaGo and transferring to other domainsMachine Learning vs Deep LearningBinary Classifier making only one predictionCreating an easy but not trivial datasetSeveral fundamental questions about CNNHow to add non-image features along side images as the input of CNNsRunning two stage classification to predict relatively rare event?Poor performance of SVM after training for rare eventsconvLSTM : how to structure input data
$begingroup$
Say I have an imbalanced data set, and I decided to over/undersample it during model training. I would then like to predict on new records but using the original, true imbalance in the classes as an apriori for the model.
i.e my classes are distributed in a ratio of 1:100, I undersample to 1:2, but would like the model to know that class A is very rare so be careful in predicting it.
For logistic regression I am familiar with a method of intercept correction, detailed here:
My question is: what if I use deep leaning instead of logistic regression?
My reasoning is that since the last layer of the NN is basically the same a logistic regression, I can use the same method for the intercept of this layer.
Can you think of any objections? any reason this doesn't make sense? If so, how would you go about correcting the model?
Also, is there a generalized method for intercept correction for a case when I have more than 2 classes?
deep-learning class-imbalance
$endgroup$
add a comment |
$begingroup$
Say I have an imbalanced data set, and I decided to over/undersample it during model training. I would then like to predict on new records but using the original, true imbalance in the classes as an apriori for the model.
i.e my classes are distributed in a ratio of 1:100, I undersample to 1:2, but would like the model to know that class A is very rare so be careful in predicting it.
For logistic regression I am familiar with a method of intercept correction, detailed here:
My question is: what if I use deep leaning instead of logistic regression?
My reasoning is that since the last layer of the NN is basically the same a logistic regression, I can use the same method for the intercept of this layer.
Can you think of any objections? any reason this doesn't make sense? If so, how would you go about correcting the model?
Also, is there a generalized method for intercept correction for a case when I have more than 2 classes?
deep-learning class-imbalance
$endgroup$
$begingroup$
Welcome to this site! I agree with you, since the proposed correction does not make any assumption about the feature space $boldsymbolx$, therefore, all layers from input $boldsymbolx$ to the last layer, act as a transformation that produces a new feature space $boldsymbolx'$, and the rest is the same.
$endgroup$
– Esmailian
Apr 1 at 17:21
add a comment |
$begingroup$
Say I have an imbalanced data set, and I decided to over/undersample it during model training. I would then like to predict on new records but using the original, true imbalance in the classes as an apriori for the model.
i.e my classes are distributed in a ratio of 1:100, I undersample to 1:2, but would like the model to know that class A is very rare so be careful in predicting it.
For logistic regression I am familiar with a method of intercept correction, detailed here:
My question is: what if I use deep leaning instead of logistic regression?
My reasoning is that since the last layer of the NN is basically the same a logistic regression, I can use the same method for the intercept of this layer.
Can you think of any objections? any reason this doesn't make sense? If so, how would you go about correcting the model?
Also, is there a generalized method for intercept correction for a case when I have more than 2 classes?
deep-learning class-imbalance
$endgroup$
Say I have an imbalanced data set, and I decided to over/undersample it during model training. I would then like to predict on new records but using the original, true imbalance in the classes as an apriori for the model.
i.e my classes are distributed in a ratio of 1:100, I undersample to 1:2, but would like the model to know that class A is very rare so be careful in predicting it.
For logistic regression I am familiar with a method of intercept correction, detailed here:
My question is: what if I use deep leaning instead of logistic regression?
My reasoning is that since the last layer of the NN is basically the same a logistic regression, I can use the same method for the intercept of this layer.
Can you think of any objections? any reason this doesn't make sense? If so, how would you go about correcting the model?
Also, is there a generalized method for intercept correction for a case when I have more than 2 classes?
deep-learning class-imbalance
deep-learning class-imbalance
edited Apr 1 at 19:12
Ethan
701625
701625
asked Apr 1 at 17:08
Oren MatarOren Matar
111
111
$begingroup$
Welcome to this site! I agree with you, since the proposed correction does not make any assumption about the feature space $boldsymbolx$, therefore, all layers from input $boldsymbolx$ to the last layer, act as a transformation that produces a new feature space $boldsymbolx'$, and the rest is the same.
$endgroup$
– Esmailian
Apr 1 at 17:21
add a comment |
$begingroup$
Welcome to this site! I agree with you, since the proposed correction does not make any assumption about the feature space $boldsymbolx$, therefore, all layers from input $boldsymbolx$ to the last layer, act as a transformation that produces a new feature space $boldsymbolx'$, and the rest is the same.
$endgroup$
– Esmailian
Apr 1 at 17:21
$begingroup$
Welcome to this site! I agree with you, since the proposed correction does not make any assumption about the feature space $boldsymbolx$, therefore, all layers from input $boldsymbolx$ to the last layer, act as a transformation that produces a new feature space $boldsymbolx'$, and the rest is the same.
$endgroup$
– Esmailian
Apr 1 at 17:21
$begingroup$
Welcome to this site! I agree with you, since the proposed correction does not make any assumption about the feature space $boldsymbolx$, therefore, all layers from input $boldsymbolx$ to the last layer, act as a transformation that produces a new feature space $boldsymbolx'$, and the rest is the same.
$endgroup$
– Esmailian
Apr 1 at 17:21
add a comment |
0
active
oldest
votes
Your Answer
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48367%2fintercept-correction-in-deep-learning%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
0
active
oldest
votes
0
active
oldest
votes
active
oldest
votes
active
oldest
votes
Thanks for contributing an answer to Data Science Stack Exchange!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
Use MathJax to format equations. MathJax reference.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48367%2fintercept-correction-in-deep-learning%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
$begingroup$
Welcome to this site! I agree with you, since the proposed correction does not make any assumption about the feature space $boldsymbolx$, therefore, all layers from input $boldsymbolx$ to the last layer, act as a transformation that produces a new feature space $boldsymbolx'$, and the rest is the same.
$endgroup$
– Esmailian
Apr 1 at 17:21