How to convert binary classifier to multiclass classifier?
$begingroup$
I am a biggener student in Machine learning, and I want to ask if is it possible to convert a binary classifier label (y) by applying some condition on column1 to get a third situation.
I.e. Instead of having just 2 cases "Easy" and "Difficult" as output, I need to apply a condition on an additional feature in order to get as output "Easy", "Normal", "Difficult".
and I need also some keyword that can I use on Google as a request to look for solution.
machine-learning data-mining svm logistic-regression multiclass-classification
asked 17 hours ago
amal amalamal amal
102
$endgroup$
add a comment |
$begingroup$
I am a biggener student in Machine learning, and I want to ask if is it possible to convert a binary classifier label (y) by applying some condition on column1 to get a third situation.
I.e. Instead of having just 2 cases "Easy" and "Difficult" as output, I need to apply a condition on an additional feature in order to get as output "Easy", "Normal", "Difficult".
and I need also some keyword that can I use on Google as a request to look for solution.
machine-learning data-mining svm logistic-regression multiclass-classification
asked 17 hours ago
amal amalamal amal
102
$endgroup$
add a comment |
$begingroup$
I am a biggener student in Machine learning, and I want to ask if is it possible to convert a binary classifier label (y) by applying some condition on column1 to get a third situation.
I.e. Instead of having just 2 cases "Easy" and "Difficult" as output, I need to apply a condition on an additional feature in order to get as output "Easy", "Normal", "Difficult".
and I need also some keyword that can I use on Google as a request to look for solution.
machine-learning data-mining svm logistic-regression multiclass-classification
asked 17 hours ago
amal amalamal amal
102
$endgroup$
I am a biggener student in Machine learning, and I want to ask if is it possible to convert a binary classifier label (y) by applying some condition on column1 to get a third situation.
I.e. Instead of having just 2 cases "Easy" and "Difficult" as output, I need to apply a condition on an additional feature in order to get as output "Easy", "Normal", "Difficult".
and I need also some keyword that can I use on Google as a request to look for solution.
machine-learning data-mining svm logistic-regression multiclass-classification
machine-learning data-mining svm logistic-regression multiclass-classification
asked 17 hours ago
amal amalamal amal
102
asked 17 hours ago
amal amalamal amal
102
asked 17 hours ago
amal amalamal amal
102
asked 17 hours ago
amal amalamal amal
102
asked 17 hours ago
amal amalamal amal
102
102
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Yes it is.
For multiclass classification problems, you can use 2 strategies: transformation to binary and extension from binary.
In approaches based on transformation to binary, you have:
- OVA (one versus all), which is based on training k binary classifiers (k = #classes), where the i-th classifier is specialized on distinguishing the i-th class from all the other k-1 classes.
- OVO (ove versus one), which is based on training k * (k-1) / 2 classifiers, where each classifier learns to distinguish 2 classes only. When a prediction is required, each clasisfier votes on the class it thinks it's correct, and the class with more votes is selected as the output.
On the other hand, you have extension from binary approaches: some classification algorithms are already capable of dealing with these multiclass problems. Some examples: kNN, decision trees, naive bayes...
You can find a bunch of resources on this.
For more practical purposes, please check out the following resource: https://scikit-learn.org/stable/modules/multiclass.html
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço 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 |
Your Answer
StackExchange.ifUsing("editor", function () {
return StackExchange.using("mathjaxEditing", function () {
StackExchange.MarkdownEditor.creationCallbacks.add(function (editor, postfix) {
StackExchange.mathjaxEditing.prepareWmdForMathJax(editor, postfix, [["$", "$"], ["\\(","\\)"]]);
});
});
}, "mathjax-editing");
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%2f46514%2fhow-to-convert-binary-classifier-to-multiclass-classifier%23new-answer', 'question_page');
}
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
Yes it is.
For multiclass classification problems, you can use 2 strategies: transformation to binary and extension from binary.
In approaches based on transformation to binary, you have:
- OVA (one versus all), which is based on training k binary classifiers (k = #classes), where the i-th classifier is specialized on distinguishing the i-th class from all the other k-1 classes.
- OVO (ove versus one), which is based on training k * (k-1) / 2 classifiers, where each classifier learns to distinguish 2 classes only. When a prediction is required, each clasisfier votes on the class it thinks it's correct, and the class with more votes is selected as the output.
On the other hand, you have extension from binary approaches: some classification algorithms are already capable of dealing with these multiclass problems. Some examples: kNN, decision trees, naive bayes...
You can find a bunch of resources on this.
For more practical purposes, please check out the following resource: https://scikit-learn.org/stable/modules/multiclass.html
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço 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 |
$begingroup$
Yes it is.
For multiclass classification problems, you can use 2 strategies: transformation to binary and extension from binary.
In approaches based on transformation to binary, you have:
- OVA (one versus all), which is based on training k binary classifiers (k = #classes), where the i-th classifier is specialized on distinguishing the i-th class from all the other k-1 classes.
- OVO (ove versus one), which is based on training k * (k-1) / 2 classifiers, where each classifier learns to distinguish 2 classes only. When a prediction is required, each clasisfier votes on the class it thinks it's correct, and the class with more votes is selected as the output.
On the other hand, you have extension from binary approaches: some classification algorithms are already capable of dealing with these multiclass problems. Some examples: kNN, decision trees, naive bayes...
You can find a bunch of resources on this.
For more practical purposes, please check out the following resource: https://scikit-learn.org/stable/modules/multiclass.html
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço 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 |
$begingroup$
Yes it is.
For multiclass classification problems, you can use 2 strategies: transformation to binary and extension from binary.
In approaches based on transformation to binary, you have:
- OVA (one versus all), which is based on training k binary classifiers (k = #classes), where the i-th classifier is specialized on distinguishing the i-th class from all the other k-1 classes.
- OVO (ove versus one), which is based on training k * (k-1) / 2 classifiers, where each classifier learns to distinguish 2 classes only. When a prediction is required, each clasisfier votes on the class it thinks it's correct, and the class with more votes is selected as the output.
On the other hand, you have extension from binary approaches: some classification algorithms are already capable of dealing with these multiclass problems. Some examples: kNN, decision trees, naive bayes...
You can find a bunch of resources on this.
For more practical purposes, please check out the following resource: https://scikit-learn.org/stable/modules/multiclass.html
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
Yes it is.
For multiclass classification problems, you can use 2 strategies: transformation to binary and extension from binary.
In approaches based on transformation to binary, you have:
- OVA (one versus all), which is based on training k binary classifiers (k = #classes), where the i-th classifier is specialized on distinguishing the i-th class from all the other k-1 classes.
- OVO (ove versus one), which is based on training k * (k-1) / 2 classifiers, where each classifier learns to distinguish 2 classes only. When a prediction is required, each clasisfier votes on the class it thinks it's correct, and the class with more votes is selected as the output.
On the other hand, you have extension from binary approaches: some classification algorithms are already capable of dealing with these multiclass problems. Some examples: kNN, decision trees, naive bayes...
You can find a bunch of resources on this.
For more practical purposes, please check out the following resource: https://scikit-learn.org/stable/modules/multiclass.html
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
answered 10 hours ago
Fábio ColaçoFábio Colaço
462
462
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Fábio Colaço is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
add a comment |
add a comment |
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%2f46514%2fhow-to-convert-binary-classifier-to-multiclass-classifier%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
