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Is there a model that can adapt to additional new training data with different columns?
2019 Community Moderator ElectionError::Type of predictors in new data do not match that of the training dataCan we use a model that overfits?Training data from different sourcesHow to handle data collecting bias in machine model trainingHow to use machine learning to extract product info from the titles of eBay listingsnp.c_ converts data type to object. Can I prevent that?Training with data of different shapes. Is padding an alternative?Can I use the training rows multiple times while training with different labels attached to it?Feeding machine learning model with different matrix
$begingroup$
My training data comes in batches. Sometimes, new batches (completely new samples) come with new columns that are not in old batches, or they may be missing some of the old columns.
For example, suppose there are two ingestions. In the 1st ingestion, we have ETL on a set of fields. In the 2nd ingestion, we have added a new field and we are not allowed to ingest and update the old records again (they may have been deleted for good).
Ideally, I want to train a classifier using all batches of data. What kind of algorithms would perform well under this scenario.
machine-learning data-cleaning machine-learning-model
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
$endgroup$
add a comment |
$begingroup$
My training data comes in batches. Sometimes, new batches (completely new samples) come with new columns that are not in old batches, or they may be missing some of the old columns.
For example, suppose there are two ingestions. In the 1st ingestion, we have ETL on a set of fields. In the 2nd ingestion, we have added a new field and we are not allowed to ingest and update the old records again (they may have been deleted for good).
Ideally, I want to train a classifier using all batches of data. What kind of algorithms would perform well under this scenario.
machine-learning data-cleaning machine-learning-model
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
$endgroup$
add a comment |
$begingroup$
My training data comes in batches. Sometimes, new batches (completely new samples) come with new columns that are not in old batches, or they may be missing some of the old columns.
For example, suppose there are two ingestions. In the 1st ingestion, we have ETL on a set of fields. In the 2nd ingestion, we have added a new field and we are not allowed to ingest and update the old records again (they may have been deleted for good).
Ideally, I want to train a classifier using all batches of data. What kind of algorithms would perform well under this scenario.
machine-learning data-cleaning machine-learning-model
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
$endgroup$
My training data comes in batches. Sometimes, new batches (completely new samples) come with new columns that are not in old batches, or they may be missing some of the old columns.
For example, suppose there are two ingestions. In the 1st ingestion, we have ETL on a set of fields. In the 2nd ingestion, we have added a new field and we are not allowed to ingest and update the old records again (they may have been deleted for good).
Ideally, I want to train a classifier using all batches of data. What kind of algorithms would perform well under this scenario.
machine-learning data-cleaning machine-learning-model
machine-learning data-cleaning machine-learning-model
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
asked Mar 26 at 8:24
kakarukeyskakarukeys
1062
1062
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
A tree-based algorithm can do that.
The point is that you need to train the model with the union of the possible columns that can exist the different batches.
Moreover you need to account for missing values so that the model can learn to recognize a missing and handle them: you need to recode the missings with a proper value, for example you can create a new level for categorical variables and recode the numerical in the standard way (zero, mean, extreme value, etc.)
answered Mar 26 at 9:54
VD93VD93
111
$endgroup$
$begingroup$
Suppose we can replace missing values, do trees have an advantage over other algos?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can't, is there an algo that can take records of varying dimension?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
You better apply some preprocessing after the ETL to create a dataset that suits the model rules. on how to handle missings take a look here stats.stackexchange.com/questions/103500/…
$endgroup$
– VD93
Mar 26 at 11:06
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
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active
oldest
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active
oldest
votes
$begingroup$
A tree-based algorithm can do that.
The point is that you need to train the model with the union of the possible columns that can exist the different batches.
Moreover you need to account for missing values so that the model can learn to recognize a missing and handle them: you need to recode the missings with a proper value, for example you can create a new level for categorical variables and recode the numerical in the standard way (zero, mean, extreme value, etc.)
answered Mar 26 at 9:54
VD93VD93
111
$endgroup$
$begingroup$
Suppose we can replace missing values, do trees have an advantage over other algos?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can't, is there an algo that can take records of varying dimension?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
You better apply some preprocessing after the ETL to create a dataset that suits the model rules. on how to handle missings take a look here stats.stackexchange.com/questions/103500/…
$endgroup$
– VD93
Mar 26 at 11:06
add a comment |
$begingroup$
A tree-based algorithm can do that.
The point is that you need to train the model with the union of the possible columns that can exist the different batches.
Moreover you need to account for missing values so that the model can learn to recognize a missing and handle them: you need to recode the missings with a proper value, for example you can create a new level for categorical variables and recode the numerical in the standard way (zero, mean, extreme value, etc.)
answered Mar 26 at 9:54
VD93VD93
111
$endgroup$
$begingroup$
Suppose we can replace missing values, do trees have an advantage over other algos?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can't, is there an algo that can take records of varying dimension?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
You better apply some preprocessing after the ETL to create a dataset that suits the model rules. on how to handle missings take a look here stats.stackexchange.com/questions/103500/…
$endgroup$
– VD93
Mar 26 at 11:06
add a comment |
$begingroup$
A tree-based algorithm can do that.
The point is that you need to train the model with the union of the possible columns that can exist the different batches.
Moreover you need to account for missing values so that the model can learn to recognize a missing and handle them: you need to recode the missings with a proper value, for example you can create a new level for categorical variables and recode the numerical in the standard way (zero, mean, extreme value, etc.)
answered Mar 26 at 9:54
VD93VD93
111
$endgroup$
A tree-based algorithm can do that.
The point is that you need to train the model with the union of the possible columns that can exist the different batches.
Moreover you need to account for missing values so that the model can learn to recognize a missing and handle them: you need to recode the missings with a proper value, for example you can create a new level for categorical variables and recode the numerical in the standard way (zero, mean, extreme value, etc.)
answered Mar 26 at 9:54
VD93VD93
111
answered Mar 26 at 9:54
VD93VD93
111
answered Mar 26 at 9:54
VD93VD93
111
answered Mar 26 at 9:54
VD93VD93
111
111
$begingroup$
Suppose we can replace missing values, do trees have an advantage over other algos?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can't, is there an algo that can take records of varying dimension?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
You better apply some preprocessing after the ETL to create a dataset that suits the model rules. on how to handle missings take a look here stats.stackexchange.com/questions/103500/…
$endgroup$
– VD93
Mar 26 at 11:06
add a comment |
$begingroup$
Suppose we can replace missing values, do trees have an advantage over other algos?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can't, is there an algo that can take records of varying dimension?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
You better apply some preprocessing after the ETL to create a dataset that suits the model rules. on how to handle missings take a look here stats.stackexchange.com/questions/103500/…
$endgroup$
– VD93
Mar 26 at 11:06
$begingroup$
Suppose we can replace missing values, do trees have an advantage over other algos?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can replace missing values, do trees have an advantage over other algos?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can't, is there an algo that can take records of varying dimension?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
Suppose we can't, is there an algo that can take records of varying dimension?
$endgroup$
– kakarukeys
Mar 26 at 10:31
$begingroup$
You better apply some preprocessing after the ETL to create a dataset that suits the model rules. on how to handle missings take a look here stats.stackexchange.com/questions/103500/…
$endgroup$
– VD93
Mar 26 at 11:06
$begingroup$
You better apply some preprocessing after the ETL to create a dataset that suits the model rules. on how to handle missings take a look here stats.stackexchange.com/questions/103500/…
$endgroup$
– VD93
Mar 26 at 11:06
add a comment |
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