Hfrhyu

Why use gamma over alpha radiation?

Stop battery usage [Ubuntu 18]

Unexpected result with right shift after bitwise negation

How does modal jazz use chord progressions?

How do I keep my slimes from escaping their pens?

Is there a documented rationale why the House Ways and Means chairman can demand tax info?

Estimated State payment too big --> money back; + 2018 Tax Reform

Do we know why communications with Beresheet and NASA were lost during the attempted landing of the Moon lander?

Limit for e and 1/e

Slither Like a Snake

What kind of display is this?

Replacing HDD with SSD; what about non-APFS/APFS?

Cold is to Refrigerator as warm is to?

Fishing simulator

How do you clear the ApexPages.getMessages() collection in a test?

When is phishing education going too far?

Can I throw a longsword at someone?

Complexity of many constant time steps with occasional logarithmic steps

How to say that you spent the night with someone, you were only sleeping and nothing else?

How are presidential pardons supposed to be used?

Statistical model of ligand substitution

Can a zero nonce be safely used with AES-GCM if the key is random and never used again?

Blender game recording at the wrong time

How is simplicity better than precision and clarity in prose?

How to handle columns with categorical data and many unique values

4

1

$begingroup$

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

1

$begingroup$

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
1

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

1,5941314

$endgroup$

add a comment | 

Your Answer

StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "557"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);

StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);

else
createEditor();

);

function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: false,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: null,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);



);

draft saved

draft discarded

Sign up or log in

StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);

Sign up using Google

Sign up using Facebook

Sign up using Email and Password

Post as a guest

Name

Email

Required, but never shown

StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fdatascience.stackexchange.com%2fquestions%2f48875%2fhow-to-handle-columns-with-categorical-data-and-many-unique-values%23new-answer', 'question_page');

);

Post as a guest

Name

Email

Required, but never shown

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

1,5941314

$endgroup$

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

1,5941314

$endgroup$

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

1,5941314

$endgroup$

share|improve this answer

edited Apr 8 at 15:10

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5941314

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5941314

answered Apr 8 at 12:05

Shamit VermaShamit Verma

1,5941314