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How to handle columns with categorical data and many unique values
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$begingroup$
I have a column with categorical data with nunique 3349 values, in a 18000k row dataset, which represent cities of the world.
I also have another column with 145 nunique values that I could also use in my model that represents product category.
Can I use one hot encoding to these columns or there's a problem with that solution?
Like which is the max number of unique values to use one hot encoding so there's not gonna be any problem ?
Can you point me to the right direction if I should use another encoding also?
machine-learning data categorical-data encoding
asked 18 hours ago
dungeondungeon
293
$endgroup$
add a comment |
$begingroup$
I have a column with categorical data with nunique 3349 values, in a 18000k row dataset, which represent cities of the world.
I also have another column with 145 nunique values that I could also use in my model that represents product category.
Can I use one hot encoding to these columns or there's a problem with that solution?
Like which is the max number of unique values to use one hot encoding so there's not gonna be any problem ?
Can you point me to the right direction if I should use another encoding also?
machine-learning data categorical-data encoding
asked 18 hours ago
dungeondungeon
293
$endgroup$
add a comment |
$begingroup$
I have a column with categorical data with nunique 3349 values, in a 18000k row dataset, which represent cities of the world.
I also have another column with 145 nunique values that I could also use in my model that represents product category.
Can I use one hot encoding to these columns or there's a problem with that solution?
Like which is the max number of unique values to use one hot encoding so there's not gonna be any problem ?
Can you point me to the right direction if I should use another encoding also?
machine-learning data categorical-data encoding
asked 18 hours ago
dungeondungeon
293
$endgroup$
I have a column with categorical data with nunique 3349 values, in a 18000k row dataset, which represent cities of the world.
I also have another column with 145 nunique values that I could also use in my model that represents product category.
Can I use one hot encoding to these columns or there's a problem with that solution?
Like which is the max number of unique values to use one hot encoding so there's not gonna be any problem ?
Can you point me to the right direction if I should use another encoding also?
machine-learning data categorical-data encoding
machine-learning data categorical-data encoding
asked 18 hours ago
dungeondungeon
293
asked 18 hours ago
dungeondungeon
293
asked 18 hours ago
dungeondungeon
293
asked 18 hours ago
dungeondungeon
293
asked 18 hours ago
dungeondungeon
293
293
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
For categorical columns, you have two options :
- Entity Embeddings
- One Hot Vector
For a column with 145 values, I would use one hot encoding and Embedding for ~3k values. This decision might change depending on overall number of features.
Embeddings map feature values into a 1D vector so that model knows NYC, Paris, London are similar cities in one aspect (size) and very different in other aspects. So, instead of using ~3k column of features, model will have ~50 columns of vector representation.
Articles that explain Embeddings :
An Overview of Categorical Input Handling for Neural Networks
On learning embeddings for categorical data using Keras
Google Developers > Machine Learning > Embeddings: Categorical Input Data
Exploring Embeddings for Categorical Variables with Keras by Florian Teschner
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
$endgroup$
add a comment |
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1 Answer
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1 Answer
1
active
oldest
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$begingroup$
For categorical columns, you have two options :
- Entity Embeddings
- One Hot Vector
For a column with 145 values, I would use one hot encoding and Embedding for ~3k values. This decision might change depending on overall number of features.
Embeddings map feature values into a 1D vector so that model knows NYC, Paris, London are similar cities in one aspect (size) and very different in other aspects. So, instead of using ~3k column of features, model will have ~50 columns of vector representation.
Articles that explain Embeddings :
An Overview of Categorical Input Handling for Neural Networks
On learning embeddings for categorical data using Keras
Google Developers > Machine Learning > Embeddings: Categorical Input Data
Exploring Embeddings for Categorical Variables with Keras by Florian Teschner
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
$endgroup$
add a comment |
$begingroup$
For categorical columns, you have two options :
- Entity Embeddings
- One Hot Vector
For a column with 145 values, I would use one hot encoding and Embedding for ~3k values. This decision might change depending on overall number of features.
Embeddings map feature values into a 1D vector so that model knows NYC, Paris, London are similar cities in one aspect (size) and very different in other aspects. So, instead of using ~3k column of features, model will have ~50 columns of vector representation.
Articles that explain Embeddings :
An Overview of Categorical Input Handling for Neural Networks
On learning embeddings for categorical data using Keras
Google Developers > Machine Learning > Embeddings: Categorical Input Data
Exploring Embeddings for Categorical Variables with Keras by Florian Teschner
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
$endgroup$
add a comment |
$begingroup$
For categorical columns, you have two options :
- Entity Embeddings
- One Hot Vector
For a column with 145 values, I would use one hot encoding and Embedding for ~3k values. This decision might change depending on overall number of features.
Embeddings map feature values into a 1D vector so that model knows NYC, Paris, London are similar cities in one aspect (size) and very different in other aspects. So, instead of using ~3k column of features, model will have ~50 columns of vector representation.
Articles that explain Embeddings :
An Overview of Categorical Input Handling for Neural Networks
On learning embeddings for categorical data using Keras
Google Developers > Machine Learning > Embeddings: Categorical Input Data
Exploring Embeddings for Categorical Variables with Keras by Florian Teschner
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
$endgroup$
For categorical columns, you have two options :
- Entity Embeddings
- One Hot Vector
For a column with 145 values, I would use one hot encoding and Embedding for ~3k values. This decision might change depending on overall number of features.
Embeddings map feature values into a 1D vector so that model knows NYC, Paris, London are similar cities in one aspect (size) and very different in other aspects. So, instead of using ~3k column of features, model will have ~50 columns of vector representation.
Articles that explain Embeddings :
An Overview of Categorical Input Handling for Neural Networks
On learning embeddings for categorical data using Keras
Google Developers > Machine Learning > Embeddings: Categorical Input Data
Exploring Embeddings for Categorical Variables with Keras by Florian Teschner
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
edited 14 hours ago
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
answered 17 hours ago
Shamit VermaShamit Verma
1,4841214
1,4841214
add a comment |
add a comment |
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