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Difference between train, test split before preprocessing and after preprocessing
Unicorn Meta Zoo #1: Why another podcast?
Announcing the arrival of Valued Associate #679: Cesar Manara
2019 Moderator Election Q&A - Questionnaire
2019 Community Moderator Election ResultsStandardScaler before and after splitting dataHow to split train/test in recommender systemsDividing data between test, learn and predictSplitting Data in scikit-learnThe model performance vary between different train-test split?Train Test Split for overlapping samplesFeaturing scaling whole data set before spliting it.StandardScaler before and after splitting dataRelationship between train and test errorSplit unprocessed dataset into train and test setsHow to handle preprocessing (StandardScaler, LabelEncoder) when using data generator to train?
$begingroup$
I am new to machine learning. I am bit confused in preprocessing. Generally,
Scenario-1: I am splitting the dataset into train,test and validation and applying the transformations like fit_transform on train and transform on test.
Scenario-2: The other method is applying transformations on the entire dataset first and then split the dataset into train,test and validation. I am bit confused in choosing , dividing the data before preprocessing and feature engineering or after preprocessing and feature engineering. Looking for a nice answer with effects and casues.
machine-learning data-science-model
asked Mar 7 at 9:49
merklemerkle
133
$endgroup$
add a comment |
$begingroup$
I am new to machine learning. I am bit confused in preprocessing. Generally,
Scenario-1: I am splitting the dataset into train,test and validation and applying the transformations like fit_transform on train and transform on test.
Scenario-2: The other method is applying transformations on the entire dataset first and then split the dataset into train,test and validation. I am bit confused in choosing , dividing the data before preprocessing and feature engineering or after preprocessing and feature engineering. Looking for a nice answer with effects and casues.
machine-learning data-science-model
asked Mar 7 at 9:49
merklemerkle
133
$endgroup$
5
$begingroup$
Hi - you want to fit the preprocessing transformations on your training set, and then apply that transformation to all of your data. This way you're not leaking information about the distribution of your test and validation sets into the transformation you're applying to your training data and whatever approach you take downstream. More details and link to crossvalidated discussion: datascience.stackexchange.com/questions/38395/…
$endgroup$
– redhqs
Mar 7 at 9:57
$begingroup$
You will first need to preprocess your dataset and then split it.
$endgroup$
– Shubham Panchal
Mar 7 at 10:37
add a comment |
$begingroup$
I am new to machine learning. I am bit confused in preprocessing. Generally,
Scenario-1: I am splitting the dataset into train,test and validation and applying the transformations like fit_transform on train and transform on test.
Scenario-2: The other method is applying transformations on the entire dataset first and then split the dataset into train,test and validation. I am bit confused in choosing , dividing the data before preprocessing and feature engineering or after preprocessing and feature engineering. Looking for a nice answer with effects and casues.
machine-learning data-science-model
asked Mar 7 at 9:49
merklemerkle
133
$endgroup$
I am new to machine learning. I am bit confused in preprocessing. Generally,
Scenario-1: I am splitting the dataset into train,test and validation and applying the transformations like fit_transform on train and transform on test.
Scenario-2: The other method is applying transformations on the entire dataset first and then split the dataset into train,test and validation. I am bit confused in choosing , dividing the data before preprocessing and feature engineering or after preprocessing and feature engineering. Looking for a nice answer with effects and casues.
machine-learning data-science-model
machine-learning data-science-model
asked Mar 7 at 9:49
merklemerkle
133
asked Mar 7 at 9:49
merklemerkle
133
edited Mar 7 at 10:43
merkle
asked Mar 7 at 9:49
merklemerkle
133
asked Mar 7 at 9:49
merklemerkle
133
asked Mar 7 at 9:49
merklemerkle
133
133
5
$begingroup$
Hi - you want to fit the preprocessing transformations on your training set, and then apply that transformation to all of your data. This way you're not leaking information about the distribution of your test and validation sets into the transformation you're applying to your training data and whatever approach you take downstream. More details and link to crossvalidated discussion: datascience.stackexchange.com/questions/38395/…
$endgroup$
– redhqs
Mar 7 at 9:57
$begingroup$
You will first need to preprocess your dataset and then split it.
$endgroup$
– Shubham Panchal
Mar 7 at 10:37
add a comment |
5
$begingroup$
Hi - you want to fit the preprocessing transformations on your training set, and then apply that transformation to all of your data. This way you're not leaking information about the distribution of your test and validation sets into the transformation you're applying to your training data and whatever approach you take downstream. More details and link to crossvalidated discussion: datascience.stackexchange.com/questions/38395/…
$endgroup$
– redhqs
Mar 7 at 9:57
$begingroup$
You will first need to preprocess your dataset and then split it.
$endgroup$
– Shubham Panchal
Mar 7 at 10:37
5
5
$begingroup$
Hi - you want to fit the preprocessing transformations on your training set, and then apply that transformation to all of your data. This way you're not leaking information about the distribution of your test and validation sets into the transformation you're applying to your training data and whatever approach you take downstream. More details and link to crossvalidated discussion: datascience.stackexchange.com/questions/38395/…
$endgroup$
– redhqs
Mar 7 at 9:57
$begingroup$
Hi - you want to fit the preprocessing transformations on your training set, and then apply that transformation to all of your data. This way you're not leaking information about the distribution of your test and validation sets into the transformation you're applying to your training data and whatever approach you take downstream. More details and link to crossvalidated discussion: datascience.stackexchange.com/questions/38395/…
$endgroup$
– redhqs
Mar 7 at 9:57
$begingroup$
You will first need to preprocess your dataset and then split it.
$endgroup$
– Shubham Panchal
Mar 7 at 10:37
$begingroup$
You will first need to preprocess your dataset and then split it.
$endgroup$
– Shubham Panchal
Mar 7 at 10:37
add a comment |
2 Answers
2
active
oldest
votes
$begingroup$
You should absolutely adopt the first scenario. That's because the transformers that you use have some parameters (e.g. mean and standard deviation in case of standard scalar) and this parameters are learned from data like the parameters of your machine learning model. As you know, you should not use the validation and test data for learning the model parameters and for the same reason, you should not use them for learning the transformer parameters. As a result, you should just use the training samples for fitting your transformer parameters if you want to try a practical machine learning scenario.
answered Apr 6 at 18:45
pythinkerpythinker
8541214
$endgroup$
add a comment |
$begingroup$
The prepossessing transformations have to be applied on all data sets ( train, test and validation). Whatever feature you add on one subset, has to be added in the other subsets.
If you're validation and test data are chosen randomly and you apply the exact transformations on all subsets both scenarios would work but the second scenario is better in some ways:
- First it easier to code you'll just have to write the transformation lines once.
- Second it is probably faster, you'll benefit from the power of numpy if you're using python for example
- Third it is less risky, take the following example. You have a categorical variable and for some reason you decide to replace the missing values in that variable with the value with highest occurrence. If the value occurrences are close you could have different value in your subsets. The same could happen if you replace a numerical value with the mean. This is unlikely if you pick your subsets randomly but you know, there is no prefect randomness.
I would recommend choosing the second option as it is easier and less risky. But still, you can try both at some point and you'll see the advantages and disadvantages of each.
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
$endgroup$
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
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oldest
votes
$begingroup$
You should absolutely adopt the first scenario. That's because the transformers that you use have some parameters (e.g. mean and standard deviation in case of standard scalar) and this parameters are learned from data like the parameters of your machine learning model. As you know, you should not use the validation and test data for learning the model parameters and for the same reason, you should not use them for learning the transformer parameters. As a result, you should just use the training samples for fitting your transformer parameters if you want to try a practical machine learning scenario.
answered Apr 6 at 18:45
pythinkerpythinker
8541214
$endgroup$
add a comment |
$begingroup$
You should absolutely adopt the first scenario. That's because the transformers that you use have some parameters (e.g. mean and standard deviation in case of standard scalar) and this parameters are learned from data like the parameters of your machine learning model. As you know, you should not use the validation and test data for learning the model parameters and for the same reason, you should not use them for learning the transformer parameters. As a result, you should just use the training samples for fitting your transformer parameters if you want to try a practical machine learning scenario.
answered Apr 6 at 18:45
pythinkerpythinker
8541214
$endgroup$
add a comment |
$begingroup$
You should absolutely adopt the first scenario. That's because the transformers that you use have some parameters (e.g. mean and standard deviation in case of standard scalar) and this parameters are learned from data like the parameters of your machine learning model. As you know, you should not use the validation and test data for learning the model parameters and for the same reason, you should not use them for learning the transformer parameters. As a result, you should just use the training samples for fitting your transformer parameters if you want to try a practical machine learning scenario.
answered Apr 6 at 18:45
pythinkerpythinker
8541214
$endgroup$
You should absolutely adopt the first scenario. That's because the transformers that you use have some parameters (e.g. mean and standard deviation in case of standard scalar) and this parameters are learned from data like the parameters of your machine learning model. As you know, you should not use the validation and test data for learning the model parameters and for the same reason, you should not use them for learning the transformer parameters. As a result, you should just use the training samples for fitting your transformer parameters if you want to try a practical machine learning scenario.
answered Apr 6 at 18:45
pythinkerpythinker
8541214
answered Apr 6 at 18:45
pythinkerpythinker
8541214
answered Apr 6 at 18:45
pythinkerpythinker
8541214
answered Apr 6 at 18:45
pythinkerpythinker
8541214
8541214
add a comment |
add a comment |
$begingroup$
The prepossessing transformations have to be applied on all data sets ( train, test and validation). Whatever feature you add on one subset, has to be added in the other subsets.
If you're validation and test data are chosen randomly and you apply the exact transformations on all subsets both scenarios would work but the second scenario is better in some ways:
- First it easier to code you'll just have to write the transformation lines once.
- Second it is probably faster, you'll benefit from the power of numpy if you're using python for example
- Third it is less risky, take the following example. You have a categorical variable and for some reason you decide to replace the missing values in that variable with the value with highest occurrence. If the value occurrences are close you could have different value in your subsets. The same could happen if you replace a numerical value with the mean. This is unlikely if you pick your subsets randomly but you know, there is no prefect randomness.
I would recommend choosing the second option as it is easier and less risky. But still, you can try both at some point and you'll see the advantages and disadvantages of each.
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
$endgroup$
add a comment |
$begingroup$
The prepossessing transformations have to be applied on all data sets ( train, test and validation). Whatever feature you add on one subset, has to be added in the other subsets.
If you're validation and test data are chosen randomly and you apply the exact transformations on all subsets both scenarios would work but the second scenario is better in some ways:
- First it easier to code you'll just have to write the transformation lines once.
- Second it is probably faster, you'll benefit from the power of numpy if you're using python for example
- Third it is less risky, take the following example. You have a categorical variable and for some reason you decide to replace the missing values in that variable with the value with highest occurrence. If the value occurrences are close you could have different value in your subsets. The same could happen if you replace a numerical value with the mean. This is unlikely if you pick your subsets randomly but you know, there is no prefect randomness.
I would recommend choosing the second option as it is easier and less risky. But still, you can try both at some point and you'll see the advantages and disadvantages of each.
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
$endgroup$
add a comment |
$begingroup$
The prepossessing transformations have to be applied on all data sets ( train, test and validation). Whatever feature you add on one subset, has to be added in the other subsets.
If you're validation and test data are chosen randomly and you apply the exact transformations on all subsets both scenarios would work but the second scenario is better in some ways:
- First it easier to code you'll just have to write the transformation lines once.
- Second it is probably faster, you'll benefit from the power of numpy if you're using python for example
- Third it is less risky, take the following example. You have a categorical variable and for some reason you decide to replace the missing values in that variable with the value with highest occurrence. If the value occurrences are close you could have different value in your subsets. The same could happen if you replace a numerical value with the mean. This is unlikely if you pick your subsets randomly but you know, there is no prefect randomness.
I would recommend choosing the second option as it is easier and less risky. But still, you can try both at some point and you'll see the advantages and disadvantages of each.
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
$endgroup$
The prepossessing transformations have to be applied on all data sets ( train, test and validation). Whatever feature you add on one subset, has to be added in the other subsets.
If you're validation and test data are chosen randomly and you apply the exact transformations on all subsets both scenarios would work but the second scenario is better in some ways:
- First it easier to code you'll just have to write the transformation lines once.
- Second it is probably faster, you'll benefit from the power of numpy if you're using python for example
- Third it is less risky, take the following example. You have a categorical variable and for some reason you decide to replace the missing values in that variable with the value with highest occurrence. If the value occurrences are close you could have different value in your subsets. The same could happen if you replace a numerical value with the mean. This is unlikely if you pick your subsets randomly but you know, there is no prefect randomness.
I would recommend choosing the second option as it is easier and less risky. But still, you can try both at some point and you'll see the advantages and disadvantages of each.
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
answered Mar 7 at 17:43
Wassim9429Wassim9429
171
171
add a comment |
add a comment |
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$begingroup$
Hi - you want to fit the preprocessing transformations on your training set, and then apply that transformation to all of your data. This way you're not leaking information about the distribution of your test and validation sets into the transformation you're applying to your training data and whatever approach you take downstream. More details and link to crossvalidated discussion: datascience.stackexchange.com/questions/38395/…
$endgroup$
– redhqs
Mar 7 at 9:57
$begingroup$
You will first need to preprocess your dataset and then split it.
$endgroup$
– Shubham Panchal
Mar 7 at 10:37