Why Naive Bayes classifier is known to be a bad estimator?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty{ margin-bottom:0;
}
$begingroup$
In scikit-learn documentation page for Naive Bayes, it states that: On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.
I want to use Naive Bayes for a classification problem of two categorical dataset, and I'm interested to know why the output of predict_proba could not be taken as a accurate prediction? The reason why I'm asking this question is that, my ultimate purpose after training this dataset is to use this trained model to predict the probabilities on a test dataset and if it does not have a good accuracy, so it seems it would not be a good fit for my case. I appreciate if someone could explain it.
machine-learning classification naive-bayes
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
$endgroup$
add a comment |
$begingroup$
In scikit-learn documentation page for Naive Bayes, it states that: On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.
I want to use Naive Bayes for a classification problem of two categorical dataset, and I'm interested to know why the output of predict_proba could not be taken as a accurate prediction? The reason why I'm asking this question is that, my ultimate purpose after training this dataset is to use this trained model to predict the probabilities on a test dataset and if it does not have a good accuracy, so it seems it would not be a good fit for my case. I appreciate if someone could explain it.
machine-learning classification naive-bayes
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
$endgroup$
add a comment |
$begingroup$
In scikit-learn documentation page for Naive Bayes, it states that: On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.
I want to use Naive Bayes for a classification problem of two categorical dataset, and I'm interested to know why the output of predict_proba could not be taken as a accurate prediction? The reason why I'm asking this question is that, my ultimate purpose after training this dataset is to use this trained model to predict the probabilities on a test dataset and if it does not have a good accuracy, so it seems it would not be a good fit for my case. I appreciate if someone could explain it.
machine-learning classification naive-bayes
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
$endgroup$
In scikit-learn documentation page for Naive Bayes, it states that: On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.
I want to use Naive Bayes for a classification problem of two categorical dataset, and I'm interested to know why the output of predict_proba could not be taken as a accurate prediction? The reason why I'm asking this question is that, my ultimate purpose after training this dataset is to use this trained model to predict the probabilities on a test dataset and if it does not have a good accuracy, so it seems it would not be a good fit for my case. I appreciate if someone could explain it.
machine-learning classification naive-bayes
machine-learning classification naive-bayes
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
asked Mar 27 at 15:48
Alone ProgrammerAlone Programmer
1184
1184
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Because it's Naive (see wikipedia): it assumes the features are independent, the probabilities are incorrect if this assumption is not correct.
eg assume you are predicting mortality based on smoking and drinking. NB may well identify people who smoke and drink as higher risk (=probability), just because there is a correlation between smoking and drinking.
Suggest you setup a contingency table for eg smoking, drinking, dying and calcuate Naive bayes. probabilities vs true probabilities
why don't you use logistic regression instead?
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
$endgroup$
$begingroup$
The reason why I wanted to use Naive Bayes first is that: in fact it does not have any hyper parameter to tune and could be a good starting point for me as non data scientist and an engineer.
$endgroup$
– Alone Programmer
Mar 27 at 16:14
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: "65"
};
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%2fstats.stackexchange.com%2fquestions%2f399722%2fwhy-naive-bayes-classifier-is-known-to-be-a-bad-estimator%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
$begingroup$
Because it's Naive (see wikipedia): it assumes the features are independent, the probabilities are incorrect if this assumption is not correct.
eg assume you are predicting mortality based on smoking and drinking. NB may well identify people who smoke and drink as higher risk (=probability), just because there is a correlation between smoking and drinking.
Suggest you setup a contingency table for eg smoking, drinking, dying and calcuate Naive bayes. probabilities vs true probabilities
why don't you use logistic regression instead?
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
$endgroup$
$begingroup$
The reason why I wanted to use Naive Bayes first is that: in fact it does not have any hyper parameter to tune and could be a good starting point for me as non data scientist and an engineer.
$endgroup$
– Alone Programmer
Mar 27 at 16:14
add a comment |
$begingroup$
Because it's Naive (see wikipedia): it assumes the features are independent, the probabilities are incorrect if this assumption is not correct.
eg assume you are predicting mortality based on smoking and drinking. NB may well identify people who smoke and drink as higher risk (=probability), just because there is a correlation between smoking and drinking.
Suggest you setup a contingency table for eg smoking, drinking, dying and calcuate Naive bayes. probabilities vs true probabilities
why don't you use logistic regression instead?
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
$endgroup$
$begingroup$
The reason why I wanted to use Naive Bayes first is that: in fact it does not have any hyper parameter to tune and could be a good starting point for me as non data scientist and an engineer.
$endgroup$
– Alone Programmer
Mar 27 at 16:14
add a comment |
$begingroup$
Because it's Naive (see wikipedia): it assumes the features are independent, the probabilities are incorrect if this assumption is not correct.
eg assume you are predicting mortality based on smoking and drinking. NB may well identify people who smoke and drink as higher risk (=probability), just because there is a correlation between smoking and drinking.
Suggest you setup a contingency table for eg smoking, drinking, dying and calcuate Naive bayes. probabilities vs true probabilities
why don't you use logistic regression instead?
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
$endgroup$
Because it's Naive (see wikipedia): it assumes the features are independent, the probabilities are incorrect if this assumption is not correct.
eg assume you are predicting mortality based on smoking and drinking. NB may well identify people who smoke and drink as higher risk (=probability), just because there is a correlation between smoking and drinking.
Suggest you setup a contingency table for eg smoking, drinking, dying and calcuate Naive bayes. probabilities vs true probabilities
why don't you use logistic regression instead?
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
answered Mar 27 at 16:09
seanv507seanv507
3,1271019
3,1271019
$begingroup$
The reason why I wanted to use Naive Bayes first is that: in fact it does not have any hyper parameter to tune and could be a good starting point for me as non data scientist and an engineer.
$endgroup$
– Alone Programmer
Mar 27 at 16:14
add a comment |
$begingroup$
The reason why I wanted to use Naive Bayes first is that: in fact it does not have any hyper parameter to tune and could be a good starting point for me as non data scientist and an engineer.
$endgroup$
– Alone Programmer
Mar 27 at 16:14
$begingroup$
The reason why I wanted to use Naive Bayes first is that: in fact it does not have any hyper parameter to tune and could be a good starting point for me as non data scientist and an engineer.
$endgroup$
– Alone Programmer
Mar 27 at 16:14
$begingroup$
The reason why I wanted to use Naive Bayes first is that: in fact it does not have any hyper parameter to tune and could be a good starting point for me as non data scientist and an engineer.
$endgroup$
– Alone Programmer
Mar 27 at 16:14
add a comment |
Thanks for contributing an answer to Cross Validated!
- 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%2fstats.stackexchange.com%2fquestions%2f399722%2fwhy-naive-bayes-classifier-is-known-to-be-a-bad-estimator%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
