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How to handle columns with categorical data and many unique values

4

0

$begingroup$

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

share|improve this question

asked Apr 8 at 11:04

dungeondungeon

394

$endgroup$

add a comment | 

4

0

$begingroup$

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

share|improve this question

asked Apr 8 at 11:04

dungeondungeon

394

$endgroup$

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

share|improve this question

asked Apr 8 at 11:04

dungeondungeon

394

$endgroup$

share|improve this question

asked Apr 8 at 11:04

dungeondungeon

394

share|improve this question

asked Apr 8 at 11:04

dungeondungeon

394

asked Apr 8 at 11:04

dungeondungeon

394

asked Apr 8 at 11:04

dungeondungeon

394

1 Answer
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active

oldest

votes

5

$begingroup$

For categorical columns, you have two options :

1. Entity Embeddings


2. 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

share|improve this answer

edited Apr 8 at 15:10

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5291314

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add a comment | 

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5

$begingroup$

For categorical columns, you have two options :

1. Entity Embeddings


2. 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

share|improve this answer

edited Apr 8 at 15:10

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5291314

$endgroup$

add a comment | 

5

$begingroup$

For categorical columns, you have two options :

1. Entity Embeddings


2. 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

share|improve this answer

edited Apr 8 at 15:10

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5291314

$endgroup$

add a comment | 

$begingroup$

For categorical columns, you have two options :

1. Entity Embeddings


2. 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

share|improve this answer

edited Apr 8 at 15:10

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5291314

$endgroup$

share|improve this answer

edited Apr 8 at 15:10

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5291314

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5291314

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5291314