4 Significant Limitations of SHAP

Summary

In this video, Connor explains the four key limitations of the SHAP (SHapley Additive exPlanations) package, a powerful tool for model understanding. These limitations include compatibility issues with certain frameworks, the impact of feature dependencies on SHAP values, its inability to support causal analysis, and human biases in interpreting SHAP plots. While SHAP is useful for model interpretation, its limitations require careful consideration to avoid drawing incorrect conclusions. Viewers are encouraged to improve their SHAP skills by accessing further resources, including a Python SHAP course linked in the description.

Takeaways

• 😀 SHAP (Shapley Additive Explanations) is a powerful package for understanding and debugging machine learning models.
• 😀 SHAP values are based on Shapley values, which are derived from game theory and help determine the contribution of each feature to model predictions.
• 😀 Despite being model-agnostic, SHAP doesn't work seamlessly across all machine learning frameworks, especially with less popular ones or more complex deep learning setups like PyTorch.
• 😀 SHAP’s open-source nature allows users to access the source code for free, but this can lead to difficulties in finding solutions to issues, particularly with less common frameworks.
• 😀 Kernel SHAP assumes that features are independent during Shapley value calculation, which can lead to incorrect interpretations when features are actually correlated.
• 😀 Feature dependencies can result in unrealistic predictions when correlated features are permutated, causing discrepancies in SHAP values.
• 😀 SHAP cannot be used for causal analysis. It reveals how important a feature is for a model's prediction but doesn't explain causal relationships with the target variable in the real world.
• 😀 Even if a model is 100% accurate, SHAP values don't guarantee causal insights; they only indicate how features affect the model’s predictions.
• 😀 Human bias is a significant challenge when interpreting SHAP plots, as confirmation bias or p-hacking can lead to misleading conclusions.
• 😀 Analysts should avoid drawing conclusions that extend beyond the model itself, especially when those conclusions could be influenced by external motivations or biases.
• 😀 SHAP’s limitations—model compatibility, feature dependency, lack of causal analysis, and potential for human misinterpretation—should encourage cautious use and interpretation of results.

Q & A

What is the Shap package used for?

The Shap package is used for understanding and debugging machine learning models. It calculates Shapley values to provide feature attribution, helping users interpret the impact of individual features on model predictions.

What are the key limitations of the Shap package discussed in the video?

The four key limitations of the Shap package discussed are: limited framework support, feature dependency issues, inability to conduct causal analysis, and human bias in interpretation of Shap plots.

Why is kernel SHAP considered model-agnostic in theory but not always in practice?

Kernel SHAP is considered model-agnostic because it can be applied to any machine learning model, but in practice, it doesn't always work for all frameworks. This is due to Shapley's lack of implementation across all machine learning packages and difficulties in handling certain models.

What issues might arise when using SHAP with less popular frameworks?

When using SHAP with less popular frameworks, users may encounter compatibility issues or difficulties getting the method to work, particularly with deep learning frameworks like PyTorch.

How does SHAP handle feature dependencies, and why can this be problematic?

SHAP assumes feature independence when permutating features to calculate Shapley values. This assumption can be problematic if features are correlated or dependent, as it may lead to unrealistic predictions and misinterpretations of feature importance.

Can you give an example of feature dependency affecting SHAP calculations?

An example of feature dependency affecting SHAP calculations is when predicting the price of a second-hand car using features like kilometers driven and car age. These features are correlated, and SHAP’s assumption of independence leads to unrealistic predictions when permutating these features outside of realistic ranges.

What is a proxy variable, and why can it lead to confusion in model interpretation?

A proxy variable is a feature that correlates with a true cause of an event, but may not directly represent the actual causal factor. This can lead to confusion when interpreting a model, as the model may use the proxy variable instead of the true cause, which might not be immediately obvious.

Why is SHAP not suitable for causal analysis?

SHAP is not suitable for causal analysis because it focuses on how important a feature is to the model’s predictions, rather than to the actual target variable. It cannot determine whether a feature is the true cause of an event or simply a correlation.

What human biases can affect the interpretation of SHAP plots?

Human biases, such as confirmation bias or the tendency to find patterns where none exist, can affect the interpretation of SHAP plots. This can lead to false narratives or incorrect conclusions, especially if the data is tortured to support a desired outcome.

What advice is given for using SHAP in practice to avoid misleading conclusions?

The advice is to maintain skepticism when interpreting SHAP values, especially when feature independence assumptions are made. Users should avoid drawing conclusions that extend beyond the model and should be aware of potential biases or ulterior motives when analyzing SHAP plots.