> For the complete documentation index, see [llms.txt](https://learn.getvectorai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://learn.getvectorai.com/what-are-vectors/untitled/applications-of-vectors/vectors-for-multi-classification.md).

# Vectors for classification

**Required Knowledge**: Vectors, Encoding, Classification problems\
**Audience**: Data scientists, Vector enthusiasts, Statisticians, Machine learning engineers\
**Reading time**: 5 minutes

**Defining Multi-Classification**

Classification refers to when a model is used to predict 2 or multiple labels (more than 2). As an example,  this could be when given an animal image, the individual is required to label the category that the animal belongs to from a list of pre-defined categories. 

**Traditional Classification**

![Traditional Multi-classification](https://imgr.whimsical.com/object/vRrKzHoiCB88pk4asEsG3)

In the classification example above, the image is read and fed through a neural network. The neural network, trained on predicting whether an image is a dog/cat/rabbit/emu is then given a probability that it can belong in each class. 

**Vectors reframe traditional classification into a vector search problem**

Let us reframe our example of image classification (labeling an image based on the given captions to identify the best category) using vector search. Instead of predicting the most likely label using a neural network (which is how it was previously done), the labels were, instead, encoded using a deep learning model. The images were also encoded and then a vector search was performed on the projections of these encodings to identify the most similar images to the labels. 

![Search Approach To Multi-Classification ](https://imgr.whimsical.com/object/WRu8JngEP6xt6vgqAYntyo)

Let us now quickly compare the advantages and disadvantages of each approach. 

**Advantages and Disadvantages of Vector Similarity Approach**

There are several advantages to this approach:

* Resolves the cold-start issue (in traditional approaches, classification neural networks would have to be re-trained in order to adapt to new categories)
* Reduced cost of data science experiments - using excellent out-of-the-box vectors/similarity search that resolves this issue means you can then reduce the cost of initial data science experiments and bring data to value quickly

Key Disadvantage:

* If you require vectors to fit well on pre-defined, it requires more data science expertise to finetune these vectors compared to traditional multi-classification approaches.


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://learn.getvectorai.com/what-are-vectors/untitled/applications-of-vectors/vectors-for-multi-classification.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.