Is it possible for a neural net to score as high as a different form of supervised learning?
$begingroup$
I've been working with the Adult Census Income dataset from UCI
http://archive.ics.uci.edu/ml/datasets/adult
I've created two different models, one using a gradient boosted classifier with sklearn, and one with a neural net using Keras/Tensorflow.
So I'm not interested in code hints or anything, but I have a general question about machine learning-I have a significantly higher accuracy using the gradient boosted classifier than I do with the neural net.
In general, is it possible for my (or any, really) neural net to reach the same accuracy as any other kind of supervised learning? Does it just take a lot of hard work and elbow grease to tune the neural net well enough? I'm working with the same dataset, using the same feature engineering for both the NN and the gradient boosted classifier.
keras scikit-learn optimization theory
asked yesterday
Amanda_PandaAmanda_Panda
1161
New contributor
Amanda_Panda 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 |
$begingroup$
I've been working with the Adult Census Income dataset from UCI
http://archive.ics.uci.edu/ml/datasets/adult
I've created two different models, one using a gradient boosted classifier with sklearn, and one with a neural net using Keras/Tensorflow.
So I'm not interested in code hints or anything, but I have a general question about machine learning-I have a significantly higher accuracy using the gradient boosted classifier than I do with the neural net.
In general, is it possible for my (or any, really) neural net to reach the same accuracy as any other kind of supervised learning? Does it just take a lot of hard work and elbow grease to tune the neural net well enough? I'm working with the same dataset, using the same feature engineering for both the NN and the gradient boosted classifier.
keras scikit-learn optimization theory
asked yesterday
Amanda_PandaAmanda_Panda
1161
New contributor
Amanda_Panda 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 |
$begingroup$
I've been working with the Adult Census Income dataset from UCI
http://archive.ics.uci.edu/ml/datasets/adult
I've created two different models, one using a gradient boosted classifier with sklearn, and one with a neural net using Keras/Tensorflow.
So I'm not interested in code hints or anything, but I have a general question about machine learning-I have a significantly higher accuracy using the gradient boosted classifier than I do with the neural net.
In general, is it possible for my (or any, really) neural net to reach the same accuracy as any other kind of supervised learning? Does it just take a lot of hard work and elbow grease to tune the neural net well enough? I'm working with the same dataset, using the same feature engineering for both the NN and the gradient boosted classifier.
keras scikit-learn optimization theory
asked yesterday
Amanda_PandaAmanda_Panda
1161
New contributor
Amanda_Panda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I've been working with the Adult Census Income dataset from UCI
http://archive.ics.uci.edu/ml/datasets/adult
I've created two different models, one using a gradient boosted classifier with sklearn, and one with a neural net using Keras/Tensorflow.
So I'm not interested in code hints or anything, but I have a general question about machine learning-I have a significantly higher accuracy using the gradient boosted classifier than I do with the neural net.
In general, is it possible for my (or any, really) neural net to reach the same accuracy as any other kind of supervised learning? Does it just take a lot of hard work and elbow grease to tune the neural net well enough? I'm working with the same dataset, using the same feature engineering for both the NN and the gradient boosted classifier.
keras scikit-learn optimization theory
keras scikit-learn optimization theory
asked yesterday
Amanda_PandaAmanda_Panda
1161
New contributor
Amanda_Panda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Amanda_PandaAmanda_Panda
1161
New contributor
Amanda_Panda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Amanda_PandaAmanda_Panda
1161
New contributor
Amanda_Panda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked yesterday
Amanda_PandaAmanda_Panda
1161
asked yesterday
Amanda_PandaAmanda_Panda
1161
1161
New contributor
Amanda_Panda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Amanda_Panda is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Amanda_Panda 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 |
1 Answer
1
active
oldest
votes
$begingroup$
Generally speak, no. Deep learning models struggle to compete when it comes to tabular data.
If we head over to kaggle were people compete to build the best model we find that usually the best performing non-ensemble models for this kind of data are gradient boosting trees. More specifically it tends to be either XGBoost or more often now days it is LightGBM that performs best. Both are highly optimized implementations of gradient boosting trees.
Feature engineering and parameter tuning are both important to get extra performance. But the gap does not shrink since you would get the same improvements if you did it on a gradient boosting model instead.
Actually other models tend to gain more from feature engineering than neural networks since one of the strengths of neural networks is that they perform a sort of automatic feature engineering when they are trained.
Neural networks usually shine in the domain of unstructured data such as text and images.
answered yesterday
Simon LarssonSimon Larsson
780114
$endgroup$
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
});
}
});
Amanda_Panda is a new contributor. Be nice, and check out our Code of Conduct.
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%2f49071%2fis-it-possible-for-a-neural-net-to-score-as-high-as-a-different-form-of-supervis%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
$begingroup$
Generally speak, no. Deep learning models struggle to compete when it comes to tabular data.
If we head over to kaggle were people compete to build the best model we find that usually the best performing non-ensemble models for this kind of data are gradient boosting trees. More specifically it tends to be either XGBoost or more often now days it is LightGBM that performs best. Both are highly optimized implementations of gradient boosting trees.
Feature engineering and parameter tuning are both important to get extra performance. But the gap does not shrink since you would get the same improvements if you did it on a gradient boosting model instead.
Actually other models tend to gain more from feature engineering than neural networks since one of the strengths of neural networks is that they perform a sort of automatic feature engineering when they are trained.
Neural networks usually shine in the domain of unstructured data such as text and images.
answered yesterday
Simon LarssonSimon Larsson
780114
$endgroup$
add a comment |
$begingroup$
Generally speak, no. Deep learning models struggle to compete when it comes to tabular data.
If we head over to kaggle were people compete to build the best model we find that usually the best performing non-ensemble models for this kind of data are gradient boosting trees. More specifically it tends to be either XGBoost or more often now days it is LightGBM that performs best. Both are highly optimized implementations of gradient boosting trees.
Feature engineering and parameter tuning are both important to get extra performance. But the gap does not shrink since you would get the same improvements if you did it on a gradient boosting model instead.
Actually other models tend to gain more from feature engineering than neural networks since one of the strengths of neural networks is that they perform a sort of automatic feature engineering when they are trained.
Neural networks usually shine in the domain of unstructured data such as text and images.
answered yesterday
Simon LarssonSimon Larsson
780114
$endgroup$
add a comment |
$begingroup$
Generally speak, no. Deep learning models struggle to compete when it comes to tabular data.
If we head over to kaggle were people compete to build the best model we find that usually the best performing non-ensemble models for this kind of data are gradient boosting trees. More specifically it tends to be either XGBoost or more often now days it is LightGBM that performs best. Both are highly optimized implementations of gradient boosting trees.
Feature engineering and parameter tuning are both important to get extra performance. But the gap does not shrink since you would get the same improvements if you did it on a gradient boosting model instead.
Actually other models tend to gain more from feature engineering than neural networks since one of the strengths of neural networks is that they perform a sort of automatic feature engineering when they are trained.
Neural networks usually shine in the domain of unstructured data such as text and images.
answered yesterday
Simon LarssonSimon Larsson
780114
$endgroup$
Generally speak, no. Deep learning models struggle to compete when it comes to tabular data.
If we head over to kaggle were people compete to build the best model we find that usually the best performing non-ensemble models for this kind of data are gradient boosting trees. More specifically it tends to be either XGBoost or more often now days it is LightGBM that performs best. Both are highly optimized implementations of gradient boosting trees.
Feature engineering and parameter tuning are both important to get extra performance. But the gap does not shrink since you would get the same improvements if you did it on a gradient boosting model instead.
Actually other models tend to gain more from feature engineering than neural networks since one of the strengths of neural networks is that they perform a sort of automatic feature engineering when they are trained.
Neural networks usually shine in the domain of unstructured data such as text and images.
answered yesterday
Simon LarssonSimon Larsson
780114
edited yesterday
answered yesterday
Simon LarssonSimon Larsson
780114
answered yesterday
Simon LarssonSimon Larsson
780114
answered yesterday
Simon LarssonSimon Larsson
780114
780114
add a comment |
add a comment |
Amanda_Panda is a new contributor. Be nice, and check out our Code of Conduct.
Amanda_Panda is a new contributor. Be nice, and check out our Code of Conduct.
Amanda_Panda is a new contributor. Be nice, and check out our Code of Conduct.
Amanda_Panda is a new contributor. Be nice, and check out our Code of Conduct.
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%2f49071%2fis-it-possible-for-a-neural-net-to-score-as-high-as-a-different-form-of-supervis%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
