Zingg-0.3.3
  • Welcome to Zingg
  • Step By Step Guide
    • Installation
      • Working with Docker Image
    • Hardware Sizing
    • Configuration
    • Creating training data
      • findTrainingData
      • label
      • findAndLabel
      • Using preexisting training data
      • Exporting labeled data as csv
    • Building and saving the model
    • Finding the matches
    • Linking across datasets
  • Data Sources and Sinks
    • Zingg Pipes
    • Snowflake
    • Cassandra
    • MongoDB
    • Neo4j
    • Parquet
  • Running Zingg on Cloud
    • Running on AWS
    • Running on Azure
    • Running on Databricks
  • Zingg Models
    • Pretrained models
  • Improving Accuracy By Defining Own Functions
  • Generating Documentation
  • Output Scores
  • Security And Privacy
  • Updating Labeled Pairs
  • Reporting bugs and contributing
  • Community
  • Frequently Asked Questions
  • Reading Material
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Improving Accuracy By Defining Own Functions

PreviousPretrained modelsNextGenerating Documentation

Last updated 2 years ago

You can add your own which will be evaluated by Zingg to build the

These business specific blocking functions go into and need to be added to and

Also, for similarity, you can define your own measures. Each dataType has predefined features, for example fuzzy type is configured for affine and jaro

You can define your own and use them

blocking functions
blocking tree.
Hash Functions
HashFunctionRegistry
hash functions config
String
comparisons