🦉
Arkangel AI Docs
  • 👋Welcome to Arkangel AI
  • 🍕Preparing Learning Data
    • Data Best Practices
    • How to anonymize your data?
    • How to build a good dataset for ML?
  • 🛠️Getting Started
  • Product Tutorials
    • 📩Upload Data
    • ⭐Improve Data
      • Handling dates
      • Correlation and Significance
      • Handling of Outliers
    • 🤖Create AI Models
    • 🔮Make Predictions
    • 📈Integrate & Monitor
  • API Docs
    • 🚩API Overview
    • 🔑Authentication
    • 👾Methods
      • 🚀Projects
      • 🧠Datasets
      • 🔮Predictions
    • 📖Glossary
Powered by GitBook
On this page
  • What are outliers?
  • How Arkangel detects outliers?

Was this helpful?

  1. Product Tutorials
  2. Improve Data

Handling of Outliers

How to detect and handle anomalous data that could potentially lower the algorithm analysis.

What are outliers?

Outliers are data points that are significantly different from the dataset, can either be abnormally high or low. They can be generated by wrong observations or inconsistent data entry and can often skew the results of statistical analyses on the dataset.

It is important to remove the rows considered as outliers since keeping them could lead to a less effective and less useful models.

How Arkangel detects outliers?

Outliers are detected by using Isolation Forests, this method filters the data by realizing cuts along the dataset, separating them by how apart the data is from one another, to the point where only a single point is left.

Anomalies entries are determined by how easily the entry is to make it isolated from the dataset.

PreviousCorrelation and SignificanceNextCreate AI Models

Last updated 2 years ago

Was this helpful?

⭐