Impact of Embedding Dimensionality on Machine Learning Interpretability | Slides

Read and see slides describing significant impact of embedding dimensionality on the interpretability and explainability of machine learning models. It highlights the trade-off between interpretability, explainability, and performance, emphasizing that while lower-dimensional spaces may be simpler to understand, they may lack accuracy, and higher-dimensional spaces, though more accurate, may be complex to interpret.
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The Impact of Embedding Dimensionality on Interpretability and Explainability

Embedding dimensionality is a critical aspect of machine learning models, particularly those that use deep learning techniques. It refers to the number of dimensions in the vector space where the model maps its input data. The choice of embedding dimensionality can significantly impact the interpretability and explainability of the model's output.

Interpretability

Explainability

Interpretability refers to the extent to which a human can understand the cause of a decision made by a machine learning model. A model with high interpretability is transparent, meaning its internal workings can be understood by humans. The embedding dimensionality can affect interpretability. For instance, a lower-dimensional space may be easier for humans to understand, but it may not capture all the nuances of the data. On the other hand, a higher-dimensional space may capture more information, but it can be harder for humans to interpret.

Explainability, on the other hand, refers to the extent to which the internal workings of a machine learning model can be explained in human terms. A model with high explainability can provide clear reasons for its decisions, which can be understood by humans. The embedding dimensionality can also impact explainability. For instance, a model that uses a lower-dimensional space may be able to provide simpler explanations for its decisions, but these explanations may not always be accurate. Conversely, a model that uses a higher-dimensional space may provide more accurate explanations, but these may be more complex and harder for humans to understand.

In conclusion, the choice of embedding dimensionality in a machine learning model can significantly impact its interpretability and explainability. While lower-dimensional spaces may be easier for humans to understand and interpret, they may not always provide the most accurate or comprehensive explanations. Conversely, higher-dimensional spaces may provide more accurate and comprehensive explanations, but they may be harder for humans to understand and interpret. Therefore, when designing machine learning models, it is important to carefully consider the trade-off between interpretability, explainability, and performance.



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