Aporia Documentation
Get StartedBook a Demo🚀 Cool StuffBlog
V2
V2
  • 📖Aporia Docs
  • 🤗Introduction
    • Quickstart
    • Support
  • 💡Core Concepts
    • Why Monitor ML Models?
    • Understanding Data Drift
    • Analyzing Performance
    • Tracking Data Segments
    • Models & Versions
  • 🚀Deployment
    • AWS
    • Google Cloud
    • Azure
    • Databricks
    • Offline / On-Prem
    • Platform Architecture
  • 🏠Storing your Predictions
    • Overview
    • Real-time Models (Postgres)
    • Real-time Models (Kafka)
    • Batch Models
    • Kubeflow / KServe
  • 🧠Model Types
    • Regression
    • Binary Classification
    • Multiclass Classification
    • Multi-Label Classification
    • Ranking
  • 🌈Explainability
    • SHAP values
  • 📜NLP
    • Intro to NLP Monitoring
    • Example: Text Classification
    • Example: Token Classification
    • Example: Question Answering
  • 🍪Data Sources
    • Overview
    • Amazon S3
    • Athena
    • BigQuery
    • Databricks
    • Glue Data Catalog
    • Google Cloud Storage
    • PostgreSQL
    • Redshift
    • Snowflake
    • Microsoft SQL Server
    • Oracle
  • ⚡Monitors & Alerts
    • Overview
    • Data Drift
    • Metric Change
    • Missing Values
    • Model Activity
    • Model Staleness
    • Performance Degradation
    • Prediction Drift
    • Value Range
    • Custom Metric
    • New Values
    • Alerts Consolidation
  • 🎨Dashboards
    • Overview
  • 🤖ML Monitoring as Code
    • Getting started
    • Adding new models
    • Data Segments
    • Custom metrics
    • Querying metrics
    • Monitors
    • Dashboards
  • 📡Integrations
    • Slack
    • Webhook
    • Teams
    • Single Sign On (SAML)
    • Cisco
  • 🔐Administration
    • Role Based Access Control (RBAC)
  • 🔑API Reference
    • REST API
    • API Extended Reference
    • Custom Segment Syntax
    • Custom Metric Syntax
    • Code-Based Metrics
    • Metrics Glossary
  • ⏩Release Notes
    • Release Notes 2024
    • Release Notes 2023
Powered by GitBook
On this page
  1. Model Types

Multiclass Classification

PreviousBinary ClassificationNextMulti-Label Classification

Last updated 2 years ago

Multiclass classification models predict one of more than two outcomes. In Aporia, these models are represented with the multiclass model type.

Examples of multiclass classification problems:

  • Is this product a book, movie, or clothing?

  • Is this movie a romantic comedy, documentary, or thriller?

  • Which category of products is most interesting to this customer?

Data Format In DB

If you have a model with one category prediction, then your database may look like the following:

id
feature1 (numeric)
feature2 (boolean)
prediction (category)
label (category)
timestamp (datetime)

1

13.5

True

Cat

Cat

2014-10-19 10:23:54

2

-8

False

Dog

Cat

2014-10-19 10:24:24

To integrate this type of model follow our , and during the schema mapping remember to include a categorical prediction field and categorical actual field and linked them together.

Check out the for more information about how to connect from different data sources.

🧠
Quickstart
data sources section