When to split data into multiple regression models instead of one model? 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 00:00UTC (8:00pm US/Eastern) 2019 Moderator Election Q&A - Questionnaire 2019 Community Moderator Election ResultsI am trying to classify/cluster users profile but don't know how with my attributesSales Dataset to determine best model for predicting future salesPredicting a Continuous output in a dataset with categoriesModel localization: one big model vs two small modelsWhich algorithm to use to match two categories with n dimensionsHow can I make a prediction in a regression model if a category has not been observed already?Extracting meaningful features from clusters and study correlationSelecting the right time series modelHow to start building a statistical regression analysis model with multiple categorical/discrete input variables of high dimension in PythonHow to measure correlation between several categorical features and a numerical label in Python?
how can a perfect fourth interval be considered either consonant or dissonant?
Can a flute soloist sit?
Using `min_active_rowversion` for global temporary tables
What other Star Trek series did the main TNG cast show up in?
Word for: a synonym with a positive connotation?
Windows 10: How to Lock (not sleep) laptop on lid close?
Why are Marketing Cloud timestamps not stored in the same timezone as Sales Cloud?
Pretty sure I'm over complicating my loops but unsure how to simplify
Nested ellipses in tikzpicture: Chomsky hierarchy
Using dividends to reduce short term capital gains?
How do you keep chess fun when your opponent constantly beats you?
How to make Illustrator type tool selection automatically adapt with text length
Why did Peik Lin say, "I'm not an animal"?
Identify 80s or 90s comics with ripped creatures (not dwarves)
How to handle characters who are more educated than the author?
Can withdrawing asylum be illegal?
Drawing vertical/oblique lines in Metrical tree (tikz-qtree, tipa)
Do working physicists consider Newtonian mechanics to be "falsified"?
What aspect of planet earth must be changed to prevent the industrial revolution?
How do spell lists change if the party levels up without taking a long rest?
Is it ok to offer lower paid work as a trial period before negotiating for a full-time job?
How to read αἱμύλιος or when to aspirate
Accepted by European university, rejected by all American ones I applied to? Possible reasons?
Why not take a picture of a closer black hole?
When to split data into multiple regression models instead of one model?
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 00:00UTC (8:00pm US/Eastern)
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsI am trying to classify/cluster users profile but don't know how with my attributesSales Dataset to determine best model for predicting future salesPredicting a Continuous output in a dataset with categoriesModel localization: one big model vs two small modelsWhich algorithm to use to match two categories with n dimensionsHow can I make a prediction in a regression model if a category has not been observed already?Extracting meaningful features from clusters and study correlationSelecting the right time series modelHow to start building a statistical regression analysis model with multiple categorical/discrete input variables of high dimension in PythonHow to measure correlation between several categorical features and a numerical label in Python?
$begingroup$
I'm playing with regression models in scikit-learn. The goal is to predict how much inventory we should purchase for the next 90 days. My data set has hundred of product categories. Each category has many unique features that do not apply to every category.
For Example: Shirt category could have "size" and "color" features where as the category Razors could have a "number of blades" feature.
Should I split my data up by category and make a different model for each? Or is it suffient to have one model in which I keep the products category as one of the features?
machine-learning regression
asked Mar 2 at 18:24
SruleSrule
1
$endgroup$
add a comment |
$begingroup$
I'm playing with regression models in scikit-learn. The goal is to predict how much inventory we should purchase for the next 90 days. My data set has hundred of product categories. Each category has many unique features that do not apply to every category.
For Example: Shirt category could have "size" and "color" features where as the category Razors could have a "number of blades" feature.
Should I split my data up by category and make a different model for each? Or is it suffient to have one model in which I keep the products category as one of the features?
machine-learning regression
asked Mar 2 at 18:24
SruleSrule
1
$endgroup$
add a comment |
$begingroup$
I'm playing with regression models in scikit-learn. The goal is to predict how much inventory we should purchase for the next 90 days. My data set has hundred of product categories. Each category has many unique features that do not apply to every category.
For Example: Shirt category could have "size" and "color" features where as the category Razors could have a "number of blades" feature.
Should I split my data up by category and make a different model for each? Or is it suffient to have one model in which I keep the products category as one of the features?
machine-learning regression
asked Mar 2 at 18:24
SruleSrule
1
$endgroup$
I'm playing with regression models in scikit-learn. The goal is to predict how much inventory we should purchase for the next 90 days. My data set has hundred of product categories. Each category has many unique features that do not apply to every category.
For Example: Shirt category could have "size" and "color" features where as the category Razors could have a "number of blades" feature.
Should I split my data up by category and make a different model for each? Or is it suffient to have one model in which I keep the products category as one of the features?
machine-learning regression
machine-learning regression
asked Mar 2 at 18:24
SruleSrule
1
asked Mar 2 at 18:24
SruleSrule
1
asked Mar 2 at 18:24
SruleSrule
1
asked Mar 2 at 18:24
SruleSrule
1
asked Mar 2 at 18:24
SruleSrule
1
1
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
You should split them by category since their features do not apply to each category.
Under certain circumstances that perhaps you manage to group some categories together based on some business logic, then perhaps you can build less models.
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
$endgroup$
$begingroup$
Is this true even if I one hot encode my features?
$endgroup$
– Srule
Mar 3 at 13:02
$begingroup$
if you can implement it, you can try both approaches and see which is better isn't it? Sometimes we even build separate models within the same category if it improve the performance.
$endgroup$
– Siong Thye Goh
Mar 3 at 13:28
add a comment |
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%2f46538%2fwhen-to-split-data-into-multiple-regression-models-instead-of-one-model%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
$begingroup$
You should split them by category since their features do not apply to each category.
Under certain circumstances that perhaps you manage to group some categories together based on some business logic, then perhaps you can build less models.
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
$endgroup$
$begingroup$
Is this true even if I one hot encode my features?
$endgroup$
– Srule
Mar 3 at 13:02
$begingroup$
if you can implement it, you can try both approaches and see which is better isn't it? Sometimes we even build separate models within the same category if it improve the performance.
$endgroup$
– Siong Thye Goh
Mar 3 at 13:28
add a comment |
$begingroup$
You should split them by category since their features do not apply to each category.
Under certain circumstances that perhaps you manage to group some categories together based on some business logic, then perhaps you can build less models.
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
$endgroup$
$begingroup$
Is this true even if I one hot encode my features?
$endgroup$
– Srule
Mar 3 at 13:02
$begingroup$
if you can implement it, you can try both approaches and see which is better isn't it? Sometimes we even build separate models within the same category if it improve the performance.
$endgroup$
– Siong Thye Goh
Mar 3 at 13:28
add a comment |
$begingroup$
You should split them by category since their features do not apply to each category.
Under certain circumstances that perhaps you manage to group some categories together based on some business logic, then perhaps you can build less models.
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
$endgroup$
You should split them by category since their features do not apply to each category.
Under certain circumstances that perhaps you manage to group some categories together based on some business logic, then perhaps you can build less models.
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
answered Mar 3 at 4:57
Siong Thye GohSiong Thye Goh
1,408620
1,408620
$begingroup$
Is this true even if I one hot encode my features?
$endgroup$
– Srule
Mar 3 at 13:02
$begingroup$
if you can implement it, you can try both approaches and see which is better isn't it? Sometimes we even build separate models within the same category if it improve the performance.
$endgroup$
– Siong Thye Goh
Mar 3 at 13:28
add a comment |
$begingroup$
Is this true even if I one hot encode my features?
$endgroup$
– Srule
Mar 3 at 13:02
$begingroup$
if you can implement it, you can try both approaches and see which is better isn't it? Sometimes we even build separate models within the same category if it improve the performance.
$endgroup$
– Siong Thye Goh
Mar 3 at 13:28
$begingroup$
Is this true even if I one hot encode my features?
$endgroup$
– Srule
Mar 3 at 13:02
$begingroup$
Is this true even if I one hot encode my features?
$endgroup$
– Srule
Mar 3 at 13:02
$begingroup$
if you can implement it, you can try both approaches and see which is better isn't it? Sometimes we even build separate models within the same category if it improve the performance.
$endgroup$
– Siong Thye Goh
Mar 3 at 13:28
$begingroup$
if you can implement it, you can try both approaches and see which is better isn't it? Sometimes we even build separate models within the same category if it improve the performance.
$endgroup$
– Siong Thye Goh
Mar 3 at 13:28
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%2f46538%2fwhen-to-split-data-into-multiple-regression-models-instead-of-one-model%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
