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Is it ok to train the model only on the interested part of the data?
Necessity of balancing positive/negative examples in binary classification machine learning?Is it good to remove outliers from the dataset?How to evaluate data capability to train a model?Train, test split of unbalanced dataset classificationIs it always better to use the whole dataset to train the final model?samples for different objects with unique labelsHow to correctly perform data sampling for train/test split in multi-label dataset?Is it correct to use non-target values of test set to engineer new features for train set?learning curve SklearnCan I use the test dataset to select a model?
$begingroup$
Let's say I have a dataset where one feature is Car type : say 'A', 'B' and 'C'.
The test set consists of samples where car type is 'A' always.
Therefore, should I train my model only on the subset where Car type is 'A' or on the whole training set?
What are the pros and cons of both approaches?
machine-learning dataset training
asked Feb 20 at 4:31
idpd15idpd15
1314
$endgroup$
bumped to the homepage by Community♦ 7 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 |
$begingroup$
Let's say I have a dataset where one feature is Car type : say 'A', 'B' and 'C'.
The test set consists of samples where car type is 'A' always.
Therefore, should I train my model only on the subset where Car type is 'A' or on the whole training set?
What are the pros and cons of both approaches?
machine-learning dataset training
asked Feb 20 at 4:31
idpd15idpd15
1314
$endgroup$
bumped to the homepage by Community♦ 7 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 |
$begingroup$
Let's say I have a dataset where one feature is Car type : say 'A', 'B' and 'C'.
The test set consists of samples where car type is 'A' always.
Therefore, should I train my model only on the subset where Car type is 'A' or on the whole training set?
What are the pros and cons of both approaches?
machine-learning dataset training
asked Feb 20 at 4:31
idpd15idpd15
1314
$endgroup$
Let's say I have a dataset where one feature is Car type : say 'A', 'B' and 'C'.
The test set consists of samples where car type is 'A' always.
Therefore, should I train my model only on the subset where Car type is 'A' or on the whole training set?
What are the pros and cons of both approaches?
machine-learning dataset training
machine-learning dataset training
asked Feb 20 at 4:31
idpd15idpd15
1314
asked Feb 20 at 4:31
idpd15idpd15
1314
asked Feb 20 at 4:31
idpd15idpd15
1314
asked Feb 20 at 4:31
idpd15idpd15
1314
asked Feb 20 at 4:31
idpd15idpd15
1314
1314
bumped to the homepage by Community♦ 7 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♦ 7 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
1
active
oldest
votes
$begingroup$
I think it depends on your understanding of the data set.
How similar are car $A$ with car $B$ and car $C$?
Is car $A$ an electric car and car $B$ and car $C$ running on gas? Is one of them a self driving car and the others are not?
If you are training them together, I think there is an implicit assumption that their behavior are similar and you want to take advantage of that, in particular, perhaps you do not have sufficient data from car $A$ and you are hoping that you can use data from car $B$ and car $C$ to help you.
However, if car $B$ and car $C$ are very distinct and you are trying to predict accident or car failure, adding it to them might not help that much. If the design of cars are very distinct, they might not cause accident due to the same features.
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
$endgroup$
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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active
oldest
votes
active
oldest
votes
$begingroup$
I think it depends on your understanding of the data set.
How similar are car $A$ with car $B$ and car $C$?
Is car $A$ an electric car and car $B$ and car $C$ running on gas? Is one of them a self driving car and the others are not?
If you are training them together, I think there is an implicit assumption that their behavior are similar and you want to take advantage of that, in particular, perhaps you do not have sufficient data from car $A$ and you are hoping that you can use data from car $B$ and car $C$ to help you.
However, if car $B$ and car $C$ are very distinct and you are trying to predict accident or car failure, adding it to them might not help that much. If the design of cars are very distinct, they might not cause accident due to the same features.
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
$endgroup$
add a comment |
$begingroup$
I think it depends on your understanding of the data set.
How similar are car $A$ with car $B$ and car $C$?
Is car $A$ an electric car and car $B$ and car $C$ running on gas? Is one of them a self driving car and the others are not?
If you are training them together, I think there is an implicit assumption that their behavior are similar and you want to take advantage of that, in particular, perhaps you do not have sufficient data from car $A$ and you are hoping that you can use data from car $B$ and car $C$ to help you.
However, if car $B$ and car $C$ are very distinct and you are trying to predict accident or car failure, adding it to them might not help that much. If the design of cars are very distinct, they might not cause accident due to the same features.
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
$endgroup$
add a comment |
$begingroup$
I think it depends on your understanding of the data set.
How similar are car $A$ with car $B$ and car $C$?
Is car $A$ an electric car and car $B$ and car $C$ running on gas? Is one of them a self driving car and the others are not?
If you are training them together, I think there is an implicit assumption that their behavior are similar and you want to take advantage of that, in particular, perhaps you do not have sufficient data from car $A$ and you are hoping that you can use data from car $B$ and car $C$ to help you.
However, if car $B$ and car $C$ are very distinct and you are trying to predict accident or car failure, adding it to them might not help that much. If the design of cars are very distinct, they might not cause accident due to the same features.
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
$endgroup$
I think it depends on your understanding of the data set.
How similar are car $A$ with car $B$ and car $C$?
Is car $A$ an electric car and car $B$ and car $C$ running on gas? Is one of them a self driving car and the others are not?
If you are training them together, I think there is an implicit assumption that their behavior are similar and you want to take advantage of that, in particular, perhaps you do not have sufficient data from car $A$ and you are hoping that you can use data from car $B$ and car $C$ to help you.
However, if car $B$ and car $C$ are very distinct and you are trying to predict accident or car failure, adding it to them might not help that much. If the design of cars are very distinct, they might not cause accident due to the same features.
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
answered Feb 20 at 5:01
Siong Thye GohSiong Thye Goh
1,367519
1,367519
add a comment |
add a comment |
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