Should data be split into test / training prior to descriptive statistics being carried out on it?
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I have a data set that I have added to and plan to carry out some modelling with. I'm wondering whether I should split the data into test / training prior to carrying out the modelling, or if I should write the descriptive section out first then split into test/training for the modelling part.
The descriptive stuff is going to be things like percentiles, some $chi^2$ between different levels, basics like this.
The data is mainly categorical, there are around 700 rows and 30 columns.
I'm planning to carry out logistic regression and (probably) a decision tree.
categorical-data dataset descriptive-statistics
asked Dec 25 '18 at 21:07
baxxbaxx
265111
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add a comment |
$begingroup$
I have a data set that I have added to and plan to carry out some modelling with. I'm wondering whether I should split the data into test / training prior to carrying out the modelling, or if I should write the descriptive section out first then split into test/training for the modelling part.
The descriptive stuff is going to be things like percentiles, some $chi^2$ between different levels, basics like this.
The data is mainly categorical, there are around 700 rows and 30 columns.
I'm planning to carry out logistic regression and (probably) a decision tree.
categorical-data dataset descriptive-statistics
asked Dec 25 '18 at 21:07
baxxbaxx
265111
$endgroup$
add a comment |
$begingroup$
I have a data set that I have added to and plan to carry out some modelling with. I'm wondering whether I should split the data into test / training prior to carrying out the modelling, or if I should write the descriptive section out first then split into test/training for the modelling part.
The descriptive stuff is going to be things like percentiles, some $chi^2$ between different levels, basics like this.
The data is mainly categorical, there are around 700 rows and 30 columns.
I'm planning to carry out logistic regression and (probably) a decision tree.
categorical-data dataset descriptive-statistics
asked Dec 25 '18 at 21:07
baxxbaxx
265111
$endgroup$
I have a data set that I have added to and plan to carry out some modelling with. I'm wondering whether I should split the data into test / training prior to carrying out the modelling, or if I should write the descriptive section out first then split into test/training for the modelling part.
The descriptive stuff is going to be things like percentiles, some $chi^2$ between different levels, basics like this.
The data is mainly categorical, there are around 700 rows and 30 columns.
I'm planning to carry out logistic regression and (probably) a decision tree.
categorical-data dataset descriptive-statistics
categorical-data dataset descriptive-statistics
asked Dec 25 '18 at 21:07
baxxbaxx
265111
asked Dec 25 '18 at 21:07
baxxbaxx
265111
asked Dec 25 '18 at 21:07
baxxbaxx
265111
asked Dec 25 '18 at 21:07
baxxbaxx
265111
asked Dec 25 '18 at 21:07
baxxbaxx
265111
265111
add a comment |
add a comment |
1 Answer
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Data splitting often requires a sample size exceeding 20,000 to work properly, i.e., to be stable. Otherwise re-splitting the data will result in arbitrary changes of the model and also of the validation stats. And note that decision trees are not competitive with logistic regression. The bootstrap or repeated cross-validation are preferred. See my RMS book and course notes.
In terms of what you can do before model validation, anything that is masked to Y is fair game. So you can do descriptive statistics that do not examine associations between X and Y.
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
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4
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Thank's Frank. So here you're suggesting suggesting that test/training with 700 rows is pointless? In your course notes though (Data splitting, 5.3.3 from : hbiostat.org/doc/rms.pdf) you give an example of a dataset with 300 elements, where the training is 200 and the test is 100. I'm still not too sure whether, in this example, you would carry out descriptive statistics on the data prior to splitting or not.
$endgroup$
– baxx
Dec 25 '18 at 21:36
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What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
$endgroup$
– Jon
Dec 26 '18 at 2:33
1
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@FrankHarrell I didn't find it clear, and the notes that you linked seemed to contradict what you've written in your original post (to me).
$endgroup$
– baxx
Dec 26 '18 at 23:06
1
$begingroup$
Tell me the nature of the contradiction and what is unclear about the notes.
$endgroup$
– Frank Harrell
Dec 26 '18 at 23:08
1
$begingroup$
In the post you've mentioned 20,000 but not explained why. In the notes you have n = 300 for splitting. For myself that is contradictory in this context. I've probably missed something, but I'm not experienced. In the explanation about whether or not descriptives can be done you mention about anything 'masked to Y', I'm not familiar with this expression. Are you saying that anything which isn't ultimately used in the model can be used in the descriptives?
$endgroup$
– baxx
Dec 27 '18 at 12:44
|
show 4 more comments
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1 Answer
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1 Answer
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$begingroup$
Data splitting often requires a sample size exceeding 20,000 to work properly, i.e., to be stable. Otherwise re-splitting the data will result in arbitrary changes of the model and also of the validation stats. And note that decision trees are not competitive with logistic regression. The bootstrap or repeated cross-validation are preferred. See my RMS book and course notes.
In terms of what you can do before model validation, anything that is masked to Y is fair game. So you can do descriptive statistics that do not examine associations between X and Y.
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
$endgroup$
4
$begingroup$
Thank's Frank. So here you're suggesting suggesting that test/training with 700 rows is pointless? In your course notes though (Data splitting, 5.3.3 from : hbiostat.org/doc/rms.pdf) you give an example of a dataset with 300 elements, where the training is 200 and the test is 100. I'm still not too sure whether, in this example, you would carry out descriptive statistics on the data prior to splitting or not.
$endgroup$
– baxx
Dec 25 '18 at 21:36
$begingroup$
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
$endgroup$
– Jon
Dec 26 '18 at 2:33
1
$begingroup$
@FrankHarrell I didn't find it clear, and the notes that you linked seemed to contradict what you've written in your original post (to me).
$endgroup$
– baxx
Dec 26 '18 at 23:06
1
$begingroup$
Tell me the nature of the contradiction and what is unclear about the notes.
$endgroup$
– Frank Harrell
Dec 26 '18 at 23:08
1
$begingroup$
In the post you've mentioned 20,000 but not explained why. In the notes you have n = 300 for splitting. For myself that is contradictory in this context. I've probably missed something, but I'm not experienced. In the explanation about whether or not descriptives can be done you mention about anything 'masked to Y', I'm not familiar with this expression. Are you saying that anything which isn't ultimately used in the model can be used in the descriptives?
$endgroup$
– baxx
Dec 27 '18 at 12:44
|
show 4 more comments
$begingroup$
Data splitting often requires a sample size exceeding 20,000 to work properly, i.e., to be stable. Otherwise re-splitting the data will result in arbitrary changes of the model and also of the validation stats. And note that decision trees are not competitive with logistic regression. The bootstrap or repeated cross-validation are preferred. See my RMS book and course notes.
In terms of what you can do before model validation, anything that is masked to Y is fair game. So you can do descriptive statistics that do not examine associations between X and Y.
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
$endgroup$
4
$begingroup$
Thank's Frank. So here you're suggesting suggesting that test/training with 700 rows is pointless? In your course notes though (Data splitting, 5.3.3 from : hbiostat.org/doc/rms.pdf) you give an example of a dataset with 300 elements, where the training is 200 and the test is 100. I'm still not too sure whether, in this example, you would carry out descriptive statistics on the data prior to splitting or not.
$endgroup$
– baxx
Dec 25 '18 at 21:36
$begingroup$
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
$endgroup$
– Jon
Dec 26 '18 at 2:33
1
$begingroup$
@FrankHarrell I didn't find it clear, and the notes that you linked seemed to contradict what you've written in your original post (to me).
$endgroup$
– baxx
Dec 26 '18 at 23:06
1
$begingroup$
Tell me the nature of the contradiction and what is unclear about the notes.
$endgroup$
– Frank Harrell
Dec 26 '18 at 23:08
1
$begingroup$
In the post you've mentioned 20,000 but not explained why. In the notes you have n = 300 for splitting. For myself that is contradictory in this context. I've probably missed something, but I'm not experienced. In the explanation about whether or not descriptives can be done you mention about anything 'masked to Y', I'm not familiar with this expression. Are you saying that anything which isn't ultimately used in the model can be used in the descriptives?
$endgroup$
– baxx
Dec 27 '18 at 12:44
|
show 4 more comments
$begingroup$
Data splitting often requires a sample size exceeding 20,000 to work properly, i.e., to be stable. Otherwise re-splitting the data will result in arbitrary changes of the model and also of the validation stats. And note that decision trees are not competitive with logistic regression. The bootstrap or repeated cross-validation are preferred. See my RMS book and course notes.
In terms of what you can do before model validation, anything that is masked to Y is fair game. So you can do descriptive statistics that do not examine associations between X and Y.
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
$endgroup$
Data splitting often requires a sample size exceeding 20,000 to work properly, i.e., to be stable. Otherwise re-splitting the data will result in arbitrary changes of the model and also of the validation stats. And note that decision trees are not competitive with logistic regression. The bootstrap or repeated cross-validation are preferred. See my RMS book and course notes.
In terms of what you can do before model validation, anything that is masked to Y is fair game. So you can do descriptive statistics that do not examine associations between X and Y.
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
answered Dec 25 '18 at 21:29
Frank HarrellFrank Harrell
55.4k3108244
55.4k3108244
4
$begingroup$
Thank's Frank. So here you're suggesting suggesting that test/training with 700 rows is pointless? In your course notes though (Data splitting, 5.3.3 from : hbiostat.org/doc/rms.pdf) you give an example of a dataset with 300 elements, where the training is 200 and the test is 100. I'm still not too sure whether, in this example, you would carry out descriptive statistics on the data prior to splitting or not.
$endgroup$
– baxx
Dec 25 '18 at 21:36
$begingroup$
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
$endgroup$
– Jon
Dec 26 '18 at 2:33
1
$begingroup$
@FrankHarrell I didn't find it clear, and the notes that you linked seemed to contradict what you've written in your original post (to me).
$endgroup$
– baxx
Dec 26 '18 at 23:06
1
$begingroup$
Tell me the nature of the contradiction and what is unclear about the notes.
$endgroup$
– Frank Harrell
Dec 26 '18 at 23:08
1
$begingroup$
In the post you've mentioned 20,000 but not explained why. In the notes you have n = 300 for splitting. For myself that is contradictory in this context. I've probably missed something, but I'm not experienced. In the explanation about whether or not descriptives can be done you mention about anything 'masked to Y', I'm not familiar with this expression. Are you saying that anything which isn't ultimately used in the model can be used in the descriptives?
$endgroup$
– baxx
Dec 27 '18 at 12:44
|
show 4 more comments
4
$begingroup$
Thank's Frank. So here you're suggesting suggesting that test/training with 700 rows is pointless? In your course notes though (Data splitting, 5.3.3 from : hbiostat.org/doc/rms.pdf) you give an example of a dataset with 300 elements, where the training is 200 and the test is 100. I'm still not too sure whether, in this example, you would carry out descriptive statistics on the data prior to splitting or not.
$endgroup$
– baxx
Dec 25 '18 at 21:36
$begingroup$
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
$endgroup$
– Jon
Dec 26 '18 at 2:33
1
$begingroup$
@FrankHarrell I didn't find it clear, and the notes that you linked seemed to contradict what you've written in your original post (to me).
$endgroup$
– baxx
Dec 26 '18 at 23:06
1
$begingroup$
Tell me the nature of the contradiction and what is unclear about the notes.
$endgroup$
– Frank Harrell
Dec 26 '18 at 23:08
1
$begingroup$
In the post you've mentioned 20,000 but not explained why. In the notes you have n = 300 for splitting. For myself that is contradictory in this context. I've probably missed something, but I'm not experienced. In the explanation about whether or not descriptives can be done you mention about anything 'masked to Y', I'm not familiar with this expression. Are you saying that anything which isn't ultimately used in the model can be used in the descriptives?
$endgroup$
– baxx
Dec 27 '18 at 12:44
4
4
$begingroup$
Thank's Frank. So here you're suggesting suggesting that test/training with 700 rows is pointless? In your course notes though (Data splitting, 5.3.3 from : hbiostat.org/doc/rms.pdf) you give an example of a dataset with 300 elements, where the training is 200 and the test is 100. I'm still not too sure whether, in this example, you would carry out descriptive statistics on the data prior to splitting or not.
$endgroup$
– baxx
Dec 25 '18 at 21:36
$begingroup$
Thank's Frank. So here you're suggesting suggesting that test/training with 700 rows is pointless? In your course notes though (Data splitting, 5.3.3 from : hbiostat.org/doc/rms.pdf) you give an example of a dataset with 300 elements, where the training is 200 and the test is 100. I'm still not too sure whether, in this example, you would carry out descriptive statistics on the data prior to splitting or not.
$endgroup$
– baxx
Dec 25 '18 at 21:36
$begingroup$
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
$endgroup$
– Jon
Dec 26 '18 at 2:33
$begingroup$
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
$endgroup$
– Jon
Dec 26 '18 at 2:33
1
1
$begingroup$
@FrankHarrell I didn't find it clear, and the notes that you linked seemed to contradict what you've written in your original post (to me).
$endgroup$
– baxx
Dec 26 '18 at 23:06
$begingroup$
@FrankHarrell I didn't find it clear, and the notes that you linked seemed to contradict what you've written in your original post (to me).
$endgroup$
– baxx
Dec 26 '18 at 23:06
1
1
$begingroup$
Tell me the nature of the contradiction and what is unclear about the notes.
$endgroup$
– Frank Harrell
Dec 26 '18 at 23:08
$begingroup$
Tell me the nature of the contradiction and what is unclear about the notes.
$endgroup$
– Frank Harrell
Dec 26 '18 at 23:08
1
1
$begingroup$
In the post you've mentioned 20,000 but not explained why. In the notes you have n = 300 for splitting. For myself that is contradictory in this context. I've probably missed something, but I'm not experienced. In the explanation about whether or not descriptives can be done you mention about anything 'masked to Y', I'm not familiar with this expression. Are you saying that anything which isn't ultimately used in the model can be used in the descriptives?
$endgroup$
– baxx
Dec 27 '18 at 12:44
$begingroup$
In the post you've mentioned 20,000 but not explained why. In the notes you have n = 300 for splitting. For myself that is contradictory in this context. I've probably missed something, but I'm not experienced. In the explanation about whether or not descriptives can be done you mention about anything 'masked to Y', I'm not familiar with this expression. Are you saying that anything which isn't ultimately used in the model can be used in the descriptives?
$endgroup$
– baxx
Dec 27 '18 at 12:44
|
show 4 more comments
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