When to split data into multiple regression models instead of one model? The Next CEO of Stack Overflow2019 Community Moderator ElectionI 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?
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When to split data into multiple regression models instead of one model?
The Next CEO of Stack Overflow2019 Community Moderator ElectionI 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
$endgroup$
bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
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
$endgroup$
bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
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
bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
bumped to the homepage by Community♦ 14 mins ago
This question has answers that may be good or bad; the system has marked it active so that they can be reviewed.
add a comment |
add a comment |
1 Answer
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$begingroup$
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 |
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1 Answer
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1 Answer
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$begingroup$
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 |
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