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 46 mins ago
baxx
1929
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 46 mins ago
baxx
1929
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 46 mins ago
baxx
1929
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 46 mins ago
baxx
1929
asked 46 mins ago
baxx
1929
asked 46 mins ago
baxx
1929
asked 46 mins ago
baxx
1929
asked 46 mins ago
baxx
1929
1929
add a comment |
add a comment |
1 Answer
1
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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 24 mins ago
Frank Harrell
54.4k3106239
1
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
16 mins ago
add a comment |
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1 Answer
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1 Answer
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active
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 24 mins ago
Frank Harrell
54.4k3106239
1
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
16 mins 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 24 mins ago
Frank Harrell
54.4k3106239
1
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
16 mins 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 24 mins 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 24 mins ago
Frank Harrell
54.4k3106239
answered 24 mins ago
Frank Harrell
54.4k3106239
answered 24 mins ago
Frank Harrell
54.4k3106239
answered 24 mins ago
Frank Harrell
54.4k3106239
54.4k3106239
1
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
16 mins ago
add a comment |
1
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
16 mins ago
1
1
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
16 mins 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
16 mins ago
add a comment |
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