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On this page

  • EuclideanDistanceComparison
    • Purpose
    • Test Mechanism
    • Signs of High Risk
    • Strengths
    • Limitations
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  1. Test descriptions
  2. Model Validation
  3. Embeddings
  4. EuclideanDistanceComparison

EuclideanDistanceComparison

Assesses and visualizes the dissimilarity between model embeddings using Euclidean distance, providing insights into model behavior and potential redundancy or diversity.

Purpose

The Euclidean Distance Comparison test aims to analyze and compare the embeddings produced by different models. By measuring the Euclidean distance between vectors in Euclidean space, it provides a metric to assess the magnitude of dissimilarity between embeddings created by different models. This is crucial for tasks that require models to produce distinct responses or feature separations.

Test Mechanism

The test computes the embeddings for each model using the provided dataset and calculates the Euclidean distance for every possible pair of models. It generates a distance matrix where each element represents the Euclidean distance between two model embeddings. This matrix is then visualized through bar charts, showing the distance distribution for each model pair. Additionally, it compiles a table with descriptive statistics such as mean, median, standard deviation, minimum, and maximum distances for each model pair, including references to the compared models.

Signs of High Risk

  • Very high distance values could suggest that models are focusing on entirely different features or aspects of the data, which might be undesirable for ensemble methods or when a consensus is required.
  • Extremely low distances across different models might indicate redundancy, suggesting that models are not providing diverse enough perspectives on the data.

Strengths

  • Provides a clear and quantifiable measure of how different the embeddings from various models are.
  • Useful for identifying outlier models or those that behave significantly differently from others in a group.

Limitations

  • Euclidean distance can be sensitive to the scale of the data, meaning that preprocessing steps like normalization might be necessary to ensure meaningful comparisons.
  • Does not consider the orientation or angle between vectors, focusing purely on magnitude differences.
EmbeddingsVisualization2D
EuclideanDistanceHeatmap

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