Should data be split into test / training prior to descriptive statistics being carried out on it?
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 19 hours ago
baxx
20219
add a comment |
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 19 hours ago
baxx
20219
add a comment |
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 19 hours ago
baxx
20219
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 19 hours ago
baxx
20219
asked 19 hours ago
baxx
20219
asked 19 hours ago
baxx
20219
asked 19 hours ago
baxx
20219
asked 19 hours ago
baxx
20219
20219
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 19 hours ago
Frank Harrell
54.4k3106239
3
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.
– baxx
19 hours ago
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
– Jon
14 hours ago
I thought I was clear regarding which kinds of descriptive statistics are OK. Regarding the 20,000 that comes from work I did where a 17,000 patient dataset with 0.3 mortality was not large enough to prevent data splitting from giving an arbitrary result. I'm not saying the modeling and validation are fruitless here. I'm just saying that model building should not sacrifice sample size and model validation should use resampling methods, repeating all steps using Y afresh for each iteration.
– Frank Harrell
2 hours ago
add a comment |
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1 Answer
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1 Answer
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oldest
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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 19 hours ago
Frank Harrell
54.4k3106239
3
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.
– baxx
19 hours ago
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
– Jon
14 hours ago
I thought I was clear regarding which kinds of descriptive statistics are OK. Regarding the 20,000 that comes from work I did where a 17,000 patient dataset with 0.3 mortality was not large enough to prevent data splitting from giving an arbitrary result. I'm not saying the modeling and validation are fruitless here. I'm just saying that model building should not sacrifice sample size and model validation should use resampling methods, repeating all steps using Y afresh for each iteration.
– Frank Harrell
2 hours ago
add a comment |
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 19 hours ago
Frank Harrell
54.4k3106239
3
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.
– baxx
19 hours ago
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
– Jon
14 hours ago
I thought I was clear regarding which kinds of descriptive statistics are OK. Regarding the 20,000 that comes from work I did where a 17,000 patient dataset with 0.3 mortality was not large enough to prevent data splitting from giving an arbitrary result. I'm not saying the modeling and validation are fruitless here. I'm just saying that model building should not sacrifice sample size and model validation should use resampling methods, repeating all steps using Y afresh for each iteration.
– Frank Harrell
2 hours ago
add a comment |
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 19 hours ago
Frank Harrell
54.4k3106239
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 19 hours ago
Frank Harrell
54.4k3106239
answered 19 hours ago
Frank Harrell
54.4k3106239
answered 19 hours ago
Frank Harrell
54.4k3106239
answered 19 hours ago
Frank Harrell
54.4k3106239
54.4k3106239
3
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.
– baxx
19 hours ago
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
– Jon
14 hours ago
I thought I was clear regarding which kinds of descriptive statistics are OK. Regarding the 20,000 that comes from work I did where a 17,000 patient dataset with 0.3 mortality was not large enough to prevent data splitting from giving an arbitrary result. I'm not saying the modeling and validation are fruitless here. I'm just saying that model building should not sacrifice sample size and model validation should use resampling methods, repeating all steps using Y afresh for each iteration.
– Frank Harrell
2 hours ago
add a comment |
3
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.
– baxx
19 hours ago
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
– Jon
14 hours ago
I thought I was clear regarding which kinds of descriptive statistics are OK. Regarding the 20,000 that comes from work I did where a 17,000 patient dataset with 0.3 mortality was not large enough to prevent data splitting from giving an arbitrary result. I'm not saying the modeling and validation are fruitless here. I'm just saying that model building should not sacrifice sample size and model validation should use resampling methods, repeating all steps using Y afresh for each iteration.
– Frank Harrell
2 hours ago
3
3
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.
– baxx
19 hours ago
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.
– baxx
19 hours ago
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
– Jon
14 hours ago
What empirical evidence do you have to support the “20,000” sample size requirement? That seems a bit arbitrary.
– Jon
14 hours ago
I thought I was clear regarding which kinds of descriptive statistics are OK. Regarding the 20,000 that comes from work I did where a 17,000 patient dataset with 0.3 mortality was not large enough to prevent data splitting from giving an arbitrary result. I'm not saying the modeling and validation are fruitless here. I'm just saying that model building should not sacrifice sample size and model validation should use resampling methods, repeating all steps using Y afresh for each iteration.
– Frank Harrell
2 hours ago
I thought I was clear regarding which kinds of descriptive statistics are OK. Regarding the 20,000 that comes from work I did where a 17,000 patient dataset with 0.3 mortality was not large enough to prevent data splitting from giving an arbitrary result. I'm not saying the modeling and validation are fruitless here. I'm just saying that model building should not sacrifice sample size and model validation should use resampling methods, repeating all steps using Y afresh for each iteration.
– Frank Harrell
2 hours ago
add a comment |
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