<p>I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this</p>
<pre><code>user_id | gender | list of interests
--------+--------+--------------------------------
user 1  | male   | games, productivity
user 2  | female | games, lifestyle, design
user 3  | male   | travel, games, messaging
user 4  | male   | messaging, blogging, lifestyle
...
</code></pre>
<p>Since the number of unique interests are few (~500) and the number of rows are high (~67M), what are the feature engineering practices that I should follow to get an ML model score a better accuracy?</p>
<p>P.S.: Simple model with one hot/count hot vectorization yields an accuracy of ~52%</p>

CC BY-SA 4.0
2023-03-11T18:03:08.597Z
Your title suggests a question about metric but your question is asking about feature engineering for fixed metric (accuracy). Please update either the title or question
SajanGohil
CC BY-SA 4.0
2023-03-11T18:09:11.117Z
@SajanGohil updated the title
theodre7
CC BY-SA 4.0
2023-03-13T15:50:27.730Z
Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
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<machine-learning><classification><feature-selection><feature-engineering>
Best feature engineering approach for interest-based age classification
theodre7
# Best feature engineering approach for interest-based age classification

(Asked by: username_0 on 2023-03-11)

I have a dataset which has users (rows) with the list of their interests (IABs), which looks like this
user_id | gender | list of interests
--------+--------+--------------------------------
user 1  | male   | games, productivity
user 2  | female | games, lifestyle, design
user 3  | male   | travel, games, messaging
user 4  | male   | messaging, blogging, lifestyle
...

Since the number of unique interests are few (~500) and the number of rows are high (~67M), what are the feature engineering practices that I should follow to get an ML model score a better accuracy?
P.S.: Simple model with one hot/count hot vectorization yields an accuracy of ~52%

**Tags:** machine-learning, classification, feature-selection, feature-engineering

> **Comments:**
> **username_1**: Your title suggests a question about metric but your question is asking about feature engineering for fixed metric (accuracy). Please update either the title or question  
> **username_0**: @username_1 updated the title  
> **username_2**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  

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## 0 answers

