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How to choose between classification Vs regression approach?
The Next CEO of Stack Overflow2019 Community Moderator ElectionPredictive models with class value belonging to a set of observationsCategorical and ordinal feature data representation in regression analysis?Can I use categorical data and Decision Trees to regress a continuous variable?Predicting a numerical value based on past values and categorical attributesClassification problem approach with PythonIs there a way to find the weights of every feature in spark ml model?Converting a regression problem into a classification problemCost Function for evaluating a Regression ModelHow many datasets are required in a prediction problem?Can I treat text review analysis as a regression problem?
$begingroup$
I need to predict the profitability of the products of a retailer. I can either predict the absolute value of the profit the products will make (continuous outcome) or predict whether the products will make a profit or not (categorical outcome). Is there any advantage of approaching this as a classification problem rather than a regression problem or vice versa? Why?
machine-learning classification regression predictive-modeling
asked Mar 25 at 9:34
AnjuAnju
82
$endgroup$
add a comment |
$begingroup$
I need to predict the profitability of the products of a retailer. I can either predict the absolute value of the profit the products will make (continuous outcome) or predict whether the products will make a profit or not (categorical outcome). Is there any advantage of approaching this as a classification problem rather than a regression problem or vice versa? Why?
machine-learning classification regression predictive-modeling
asked Mar 25 at 9:34
AnjuAnju
82
$endgroup$
add a comment |
$begingroup$
I need to predict the profitability of the products of a retailer. I can either predict the absolute value of the profit the products will make (continuous outcome) or predict whether the products will make a profit or not (categorical outcome). Is there any advantage of approaching this as a classification problem rather than a regression problem or vice versa? Why?
machine-learning classification regression predictive-modeling
asked Mar 25 at 9:34
AnjuAnju
82
$endgroup$
I need to predict the profitability of the products of a retailer. I can either predict the absolute value of the profit the products will make (continuous outcome) or predict whether the products will make a profit or not (categorical outcome). Is there any advantage of approaching this as a classification problem rather than a regression problem or vice versa? Why?
machine-learning classification regression predictive-modeling
machine-learning classification regression predictive-modeling
asked Mar 25 at 9:34
AnjuAnju
82
asked Mar 25 at 9:34
AnjuAnju
82
asked Mar 25 at 9:34
AnjuAnju
82
asked Mar 25 at 9:34
AnjuAnju
82
asked Mar 25 at 9:34
AnjuAnju
82
82
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
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I would say that the main thing is what information you think is the most valuable. There are some differences.
The benefit with a regressor is that it can give you a number of how much profit you can expect. That is obviously useful when considering a new product. The drawback is that it cannot tell you how likely you are to see this profit. It will not tell you if a product is risky.
Many classifiers can tell you with what probability it thinks a product will be profitable. This can alert you to potentially risky products. But the drawback is that you won't get to know how big the potential profit is.
As I said, it depends what you value most. But nothing is stopping you from training two models and using information from both of them to make your decisions.
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
$endgroup$
$begingroup$
thanks for the answer. This helps.
$endgroup$
– Anju
Mar 26 at 6:05
$begingroup$
That is nice to hear, good luck!
$endgroup$
– Simon Larsson
Mar 26 at 6:08
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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$begingroup$
I would say that the main thing is what information you think is the most valuable. There are some differences.
The benefit with a regressor is that it can give you a number of how much profit you can expect. That is obviously useful when considering a new product. The drawback is that it cannot tell you how likely you are to see this profit. It will not tell you if a product is risky.
Many classifiers can tell you with what probability it thinks a product will be profitable. This can alert you to potentially risky products. But the drawback is that you won't get to know how big the potential profit is.
As I said, it depends what you value most. But nothing is stopping you from training two models and using information from both of them to make your decisions.
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
$endgroup$
$begingroup$
thanks for the answer. This helps.
$endgroup$
– Anju
Mar 26 at 6:05
$begingroup$
That is nice to hear, good luck!
$endgroup$
– Simon Larsson
Mar 26 at 6:08
add a comment |
$begingroup$
I would say that the main thing is what information you think is the most valuable. There are some differences.
The benefit with a regressor is that it can give you a number of how much profit you can expect. That is obviously useful when considering a new product. The drawback is that it cannot tell you how likely you are to see this profit. It will not tell you if a product is risky.
Many classifiers can tell you with what probability it thinks a product will be profitable. This can alert you to potentially risky products. But the drawback is that you won't get to know how big the potential profit is.
As I said, it depends what you value most. But nothing is stopping you from training two models and using information from both of them to make your decisions.
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
$endgroup$
$begingroup$
thanks for the answer. This helps.
$endgroup$
– Anju
Mar 26 at 6:05
$begingroup$
That is nice to hear, good luck!
$endgroup$
– Simon Larsson
Mar 26 at 6:08
add a comment |
$begingroup$
I would say that the main thing is what information you think is the most valuable. There are some differences.
The benefit with a regressor is that it can give you a number of how much profit you can expect. That is obviously useful when considering a new product. The drawback is that it cannot tell you how likely you are to see this profit. It will not tell you if a product is risky.
Many classifiers can tell you with what probability it thinks a product will be profitable. This can alert you to potentially risky products. But the drawback is that you won't get to know how big the potential profit is.
As I said, it depends what you value most. But nothing is stopping you from training two models and using information from both of them to make your decisions.
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
$endgroup$
I would say that the main thing is what information you think is the most valuable. There are some differences.
The benefit with a regressor is that it can give you a number of how much profit you can expect. That is obviously useful when considering a new product. The drawback is that it cannot tell you how likely you are to see this profit. It will not tell you if a product is risky.
Many classifiers can tell you with what probability it thinks a product will be profitable. This can alert you to potentially risky products. But the drawback is that you won't get to know how big the potential profit is.
As I said, it depends what you value most. But nothing is stopping you from training two models and using information from both of them to make your decisions.
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
answered Mar 25 at 12:47
Simon LarssonSimon Larsson
563112
563112
$begingroup$
thanks for the answer. This helps.
$endgroup$
– Anju
Mar 26 at 6:05
$begingroup$
That is nice to hear, good luck!
$endgroup$
– Simon Larsson
Mar 26 at 6:08
add a comment |
$begingroup$
thanks for the answer. This helps.
$endgroup$
– Anju
Mar 26 at 6:05
$begingroup$
That is nice to hear, good luck!
$endgroup$
– Simon Larsson
Mar 26 at 6:08
$begingroup$
thanks for the answer. This helps.
$endgroup$
– Anju
Mar 26 at 6:05
$begingroup$
thanks for the answer. This helps.
$endgroup$
– Anju
Mar 26 at 6:05
$begingroup$
That is nice to hear, good luck!
$endgroup$
– Simon Larsson
Mar 26 at 6:08
$begingroup$
That is nice to hear, good luck!
$endgroup$
– Simon Larsson
Mar 26 at 6:08
add a comment |
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