Predicting descrete value problem in regression or classification 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 ResultsRegression Model for explained model(Details inside)How to move forward on Regression problemConfidence intervals for binary classification probabilitiesMuti-Output Decision tree with classification and regression in outputMethod for predicting winner of call for tendersRegression problem as predicting a delta from another algorithm's outputCensored output data, which activation function for the output layer and which loss function to use?Algorithms, techniques, papers for regression with vector outputUnderstanding output of LSTM for regressionIs zero-inflated negative binomial regression appropriate for this data? Am I interpreting it correctly?
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Predicting descrete value problem in regression or classification
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 ResultsRegression Model for explained model(Details inside)How to move forward on Regression problemConfidence intervals for binary classification probabilitiesMuti-Output Decision tree with classification and regression in outputMethod for predicting winner of call for tendersRegression problem as predicting a delta from another algorithm's outputCensored output data, which activation function for the output layer and which loss function to use?Algorithms, techniques, papers for regression with vector outputUnderstanding output of LSTM for regressionIs zero-inflated negative binomial regression appropriate for this data? Am I interpreting it correctly?
$begingroup$
In machine learning, regression algorithms attempt to estimate the mapping function (f) from the input variables (x) to numerical or continuous output variables (y).
I have one usecase where I predict shift_id. Shit_Id is ID values given to different city location.
As per my understanding this is regression problem because it predict numerical value. Is this right?
Also precision, recall f1 measure can be calculated for regression problem?
machine-learning classification regression
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
$endgroup$
add a comment |
$begingroup$
In machine learning, regression algorithms attempt to estimate the mapping function (f) from the input variables (x) to numerical or continuous output variables (y).
I have one usecase where I predict shift_id. Shit_Id is ID values given to different city location.
As per my understanding this is regression problem because it predict numerical value. Is this right?
Also precision, recall f1 measure can be calculated for regression problem?
machine-learning classification regression
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
$endgroup$
add a comment |
$begingroup$
In machine learning, regression algorithms attempt to estimate the mapping function (f) from the input variables (x) to numerical or continuous output variables (y).
I have one usecase where I predict shift_id. Shit_Id is ID values given to different city location.
As per my understanding this is regression problem because it predict numerical value. Is this right?
Also precision, recall f1 measure can be calculated for regression problem?
machine-learning classification regression
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
$endgroup$
In machine learning, regression algorithms attempt to estimate the mapping function (f) from the input variables (x) to numerical or continuous output variables (y).
I have one usecase where I predict shift_id. Shit_Id is ID values given to different city location.
As per my understanding this is regression problem because it predict numerical value. Is this right?
Also precision, recall f1 measure can be calculated for regression problem?
machine-learning classification regression
machine-learning classification regression
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
asked Apr 2 at 7:21
Jhon PatricJhon Patric
185
185
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
IDs are categorical, not numeric. You should be treating this as a multi-class classification problem. Your IDs are locations, a location is a class. The ID is just a identifier for the class.
Since you have a classification problem you should be using precision, recall and f1. However, if it was regression you would have been using mean squared error, mean absolute error and possibly something else.
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
$endgroup$
$begingroup$
Thanks for such a clear answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
I dont have enough points to upvote your answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
Don't worry about that. Glad I could help. Good luck! :)
$endgroup$
– Simon Larsson
Apr 2 at 7:44
add a comment |
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1 Answer
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1 Answer
1
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oldest
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$begingroup$
IDs are categorical, not numeric. You should be treating this as a multi-class classification problem. Your IDs are locations, a location is a class. The ID is just a identifier for the class.
Since you have a classification problem you should be using precision, recall and f1. However, if it was regression you would have been using mean squared error, mean absolute error and possibly something else.
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
$endgroup$
$begingroup$
Thanks for such a clear answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
I dont have enough points to upvote your answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
Don't worry about that. Glad I could help. Good luck! :)
$endgroup$
– Simon Larsson
Apr 2 at 7:44
add a comment |
$begingroup$
IDs are categorical, not numeric. You should be treating this as a multi-class classification problem. Your IDs are locations, a location is a class. The ID is just a identifier for the class.
Since you have a classification problem you should be using precision, recall and f1. However, if it was regression you would have been using mean squared error, mean absolute error and possibly something else.
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
$endgroup$
$begingroup$
Thanks for such a clear answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
I dont have enough points to upvote your answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
Don't worry about that. Glad I could help. Good luck! :)
$endgroup$
– Simon Larsson
Apr 2 at 7:44
add a comment |
$begingroup$
IDs are categorical, not numeric. You should be treating this as a multi-class classification problem. Your IDs are locations, a location is a class. The ID is just a identifier for the class.
Since you have a classification problem you should be using precision, recall and f1. However, if it was regression you would have been using mean squared error, mean absolute error and possibly something else.
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
$endgroup$
IDs are categorical, not numeric. You should be treating this as a multi-class classification problem. Your IDs are locations, a location is a class. The ID is just a identifier for the class.
Since you have a classification problem you should be using precision, recall and f1. However, if it was regression you would have been using mean squared error, mean absolute error and possibly something else.
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
edited Apr 2 at 7:36
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
answered Apr 2 at 7:25
Simon LarssonSimon Larsson
823114
823114
$begingroup$
Thanks for such a clear answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
I dont have enough points to upvote your answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
Don't worry about that. Glad I could help. Good luck! :)
$endgroup$
– Simon Larsson
Apr 2 at 7:44
add a comment |
$begingroup$
Thanks for such a clear answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
I dont have enough points to upvote your answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
Don't worry about that. Glad I could help. Good luck! :)
$endgroup$
– Simon Larsson
Apr 2 at 7:44
$begingroup$
Thanks for such a clear answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
Thanks for such a clear answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
I dont have enough points to upvote your answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
I dont have enough points to upvote your answer
$endgroup$
– Jhon Patric
Apr 2 at 7:42
$begingroup$
Don't worry about that. Glad I could help. Good luck! :)
$endgroup$
– Simon Larsson
Apr 2 at 7:44
$begingroup$
Don't worry about that. Glad I could help. Good luck! :)
$endgroup$
– Simon Larsson
Apr 2 at 7:44
add a comment |
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