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v0.23.0
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  • ☂️Introduction
  • Model Monitoring
    • Quickstart
    • Data Preparation
      • How to get data ready for NannyML
    • Tutorials
      • Monitoring a tabular data model
      • Monitoring with segmentation
      • Monitoring a text classification model
      • Monitoring a computer vision model
    • How it works
      • Probabilistic Adaptive Performance Estimation (PAPE)
      • Reverse Concept Drift (RCD)
    • Custom Metrics
      • Creating Custom Metrics
        • Writing Functions for Binary Classification
        • Writing Functions for Multiclass Classification
        • Writing Functions for Regression
        • Handling Missing Values
        • Advanced Tutorial: Creating a MTBF Custom Metric
      • Adding a Custom Metric through NannyML SDK
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  • NannyML Cloud SDK
    • Getting Started
    • API Reference
  • Probabilistic Model Evaluation
    • Introduction
    • Tutorials
      • Evaluating a binary classification model
      • Data Preparation
    • How it works
      • HDI+ROPE (with minimum precision)
      • Getting Probability Distribution of a Performance Metric with targets
      • Getting Probability Distribution of Performance Metric without targets
      • Getting Probability Distribution of Performance Metric when some observations have labels
      • Defaults for ROPE and estimation precision
  • Experiments Module
    • Introduction
    • Tutorials
      • Running an A/B test
      • Data Preparation
    • How it works
      • Getting probability distribution of the difference of binary downstream metrics
  • miscellaneous
    • Engineering
    • Usage logging in NannyNL
    • Versions
      • Version 0.23.0
      • Version 0.22.0
      • Version 0.21.0
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  • Creating a Binary Classification Custom Metric
  • Creating a Multiclass Classification Custom Metric
  • Creating a Regression Custom Metric
  1. Model Monitoring
  2. Custom Metrics

Creating Custom Metrics

How do I create a custom metric

PreviousCustom MetricsNextWriting Functions for Binary Classification

Let's see how we can create a custom metric for each machine learning problem type. To make things more structured, we have created a separate page for each problem type. The key to creating a custom metric is to write the code for its required functions.

We also discuss more advanced topics such as and .

Creating a Binary Classification Custom Metric
Creating a Multiclass Classification Custom Metric
Creating a Regression Custom Metric
handling missing values
creating a custom MTBF metric