<p>You have a few overlapping topics here:</p>
<ol>
<li>Fitting a model to your data in Python</li>
<li>Storing model parameters to the database</li>
<li>Executing SQL queries to apply the model</li>
</ol>
<p>I would start with a linear model, such as <a href="https://en.wikipedia.org/wiki/Logistic_regression" rel="nofollow noreferrer">logistic regression</a> or <a href="https://en.wikipedia.org/wiki/Linear_discriminant_analysis" rel="nofollow noreferrer">linear discriminant analysis</a>.</p>
<p>You should think carefully how you structure the data in the database. This is more of a programming topic, but I would have a table with few columns and several rows to represent each item. This way you don't need to modify the schema if your data gets a new input feature.</p>
<p>Then as a final step you'll need to &quot;just&quot; write the SQL query, joining attribute data and model parameters and output. But this is off-topic on an AI forum.</p>
<p>Getting back to linear regression, you must re-arrange the binary representations into separate columns:</p>
<pre><code>array([[1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0],
       [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0],
       [1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0],
       [1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1],
       [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1],
       [1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1]])
</code></pre>

CC BY-SA 4.0
2022-10-25T13:58:46.357Z
2022-10-25T13:58:46.357Z
NikoNyrh
<p>What is the easiest classification algorithm in SQL when my data looks like this?</p>
<pre><code>A = 101000101110
B = 010101110010
C = 100101101000
B = 100101101101
C = 100010000001
A = 100010010101
</code></pre>
<p>These are binary vectors that indicate the presence of a certain attribute.</p>
<p>I was thinking about training some kind of regression using python that gives me a vector.</p>
<p>Then I would only need to multiple the vector with the binary vectors and I would bet some number indicating me what kind of label I have using a threshold method.</p>
<p>Are there any python algorithms that allow me to do that?</p>

CC BY-SA 4.0
2022-10-24T20:01:04.803Z
Unless SQL has changed in the last 10 years and I missed it, SQL doesn't have machine learning algorithms... It's a language for specifying a query out of a database.  I suspect you might need to reword your question
David Hoelzer
CC BY-SA 4.0
2022-10-24T23:32:04.320Z
The question, the data, and the goal are all unclear to me.  @Ohumeronen, please review this article on "How do I ask a good question?" and revise your post accordingly:  https://ai.stackexchange.com/help/how-to-ask.  You are more likely to get the help you need if you help us help you.
Snehal Patel
<machine-learning><python><linear-regression>
SQL Machine Learning using matrix multiplication
Ohumeronen
# SQL Machine Learning using matrix multiplication

(Asked by: username_0 on 2022-10-24)

What is the easiest classification algorithm in SQL when my data looks like this?
A = 101000101110
B = 010101110010
C = 100101101000
B = 100101101101
C = 100010000001
A = 100010010101

These are binary vectors that indicate the presence of a certain attribute.
I was thinking about training some kind of regression using python that gives me a vector.
Then I would only need to multiple the vector with the binary vectors and I would bet some number indicating me what kind of label I have using a threshold method.
Are there any python algorithms that allow me to do that?

**Tags:** machine-learning, python, linear-regression

> **Comments:**
> **username_1**: Unless SQL has changed in the last 10 years and I missed it, SQL doesn't have machine learning algorithms... It's a language for specifying a query out of a database.  I suspect you might need to reword your question  
> **username_2**: The question, the data, and the goal are all unclear to me.  @username_0, please review this article on "How do I ask a good question?" and revise your post accordingly:  https://ai.stackexchange.com/help/how-to-ask.  You are more likely to get the help you need if you help us help you.  

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## 1 answers

### Answer by username_3 on 2022-10-25. Score: 1

You have a few overlapping topics here:

Fitting a model to your data in Python
Storing model parameters to the database
Executing SQL queries to apply the model

I would start with a linear model, such as logistic regression or linear discriminant analysis.
You should think carefully how you structure the data in the database. This is more of a programming topic, but I would have a table with few columns and several rows to represent each item. This way you don't need to modify the schema if your data gets a new input feature.
Then as a final step you'll need to "just" write the SQL query, joining attribute data and model parameters and output. But this is off-topic on an AI forum.
Getting back to linear regression, you must re-arrange the binary representations into separate columns:
array([[1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0],
       [0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0],
       [1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0],
       [1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1],
       [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1],
       [1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1]])


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