This article discusses techniques and best practices for explaining the predictions made by tree-based, neural network, and deep learning models.
As machine learning models become more prevalent in decision-making processes, it is important to understand how these models make predictions and to be able to explain their decision-making process to a wide range of audiences. This is known as model explainability, or the ability to explain the predictions made by a model in a way that is easily understood by humans. Model explainability is important for a number of reasons, including building trust in the model, identifying biases, and improving the model’s performance.
There are two main categories of model explainability techniques: local explanation techniques and global explanation techniques.
Local explanation techniques are used to explain the reasoning behind a single prediction made by a model. These techniques provide a detailed explanation of how a particular prediction was made, and are useful for understanding the decision-making process of a model on a case-by-case basis. Some examples of local explanation techniques include:
Regression model coefficients
For regression models, the coefficients of the features can provide information about the relationship between each feature and the predicted output. A positive coefficient indicates that an increase in the feature value is associated with an increase in the prediction, while a negative coefficient indicates the opposite relationship. The magnitude of the coefficient can also provide information about the strength of the relationship.
Decision trees
Decision tree models are inherently interpretable, as they provide a clear, step-by-step breakdown of how a prediction was made.
Feature importance
This technique involves ranking the features used by the model in order of their importance in making a prediction. This can help to identify which features the model is relying on most heavily and can provide insight into the decision-making process of the model.
Partial dependence plots
These plots show the relationship between a single feature and the model’s prediction, while holding all other features constant. This can help to identify how a particular feature is affecting the model’s prediction.
Global explanation techniques, on the other hand, are used to explain the overall decision-making process of a model. These techniques provide a broad view of how the model is making predictions, and are useful for understanding the model’s behavior as a whole. Some examples of global explanation techniques include:
Model agnostic methods
These techniques can be applied to any type of model and provide an explanation of the model’s overall behavior. Examples include techniques such as LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations).
Model-specific methods
These techniques are tailored to specific types of models, such as gradient boosting models or neural networks. These techniques provide an in-depth understanding of how the model is making predictions but may be less interpretable to a general audience.
Example 1: Local explanation technique applied to a tree-based model
Suppose we have a tree based model that is being used to predict whether a customer will churn (i.e., cancel their service). The tree based model has been trained on a dataset that includes features such as the customer’s age, monthly charges, and length of tenure.
To explain the decision-making process of this model, we can use a feature importance plot to rank the features by their importance in making a prediction. In this case, we find that the most important feature is the customer’s length of tenure, followed by the monthly charges. This helps us to understand that the model is primarily using these two features to make predictions about churn.
We can also use partial dependence plots (PDPs) to understand how each of these features is impacting the model’s prediction. For example, a PDP for the length of tenure feature may show that customers with a longer tenure are less likely to churn. This helps to provide a more detailed understanding of how the model is using this feature to make predictions.
Example 2: Global explanation technique applied to a neural network
Now suppose we have a neural network model that is being used to predict the likelihood of a customer defaulting on a loan. This model has been trained on a dataset that includes features such as the customer’s credit score, income, and loan amount.
To explain the overall decision-making process of this model, we can use a technique such as LIME (Local Interpretable Model-Agnostic Explanations). LIME works by generating a simplified, interpretable model that is used to approximate the behavior of the complex neural network model. This simplified model can then be used to provide explanations for individual predictions made by the neural network.
For example, LIME might generate a linear model that includes features such as the customer’s credit score and income, with coefficients that indicate the relative importance of each feature. This would provide a high-level understanding of how the neural network is using these features to make predictions about loan default.
In addition to LIME, we can also use techniques such as SHAP (SHapley Additive exPlanations) to provide global explanations of a neural network model. SHAP works by calculating the contribution of each feature to the model’s prediction, based on the Shapley values from game theory. These values represent the marginal contribution of each feature and can provide a comprehensive understanding of how the model is using all of the features in the dataset to make predictions.
Example 3: Local explanation technique applied to a deep learning model
Now suppose we have a deep learning model that is being used to classify images of animals. This model has been trained on a dataset of images that includes features such as the color, shape, and size of the animals.
To explain the decision-making process of this model, we can use a technique such as Grad-CAM (Gradient-weighted Class Activation Mapping). Grad-CAM is a technique that can be used to visualize the features of an image that are most important to a deep learning model’s prediction. In this case, we can use Grad-CAM to understand which features of the animals in the images the model is using to make its predictions. For example, Grad-CAM might show that the model is primarily using the shape and size of the animals to classify them. This can provide a more detailed understanding of how the model is making its predictions.
When implementing model explainability, it is important to consider the needs of the intended audience and to use a combination of techniques to provide a comprehensive explanation. Some best practices to keep in mind include:
- Consider the audience and their needs: Different audiences may have different needs and preferences when it comes to understanding model explainability. For example, technical audiences may be interested in more detailed explanations that delve into the technical details of the model, while non-technical audiences may prefer simpler, more intuitive explanations. It is important to consider the needs of the intended audience when selecting and implementing model explainability techniques.
- Use multiple techniques to provide a comprehensive explanation: Different techniques provide different types of insights and can be used in conjunction to provide a more comprehensive understanding of the model’s decision-making process. For example, using both a feature importance plot and partial dependence plots can provide both a high-level view of the model’s behavior and a detailed understanding of how individual features are impacting the model’s predictions.
- Balance between simplicity and comprehensiveness: It is important to strike a balance between providing a simple, intuitive explanation that is easy to understand and a more comprehensive explanation that provides a detailed understanding of the model’s behavior. Striking this balance can help to ensure that the explanation is both understandable and informative.
Model explainability is an important aspect of machine learning, and there are a variety of techniques available for explaining the predictions made by different types of models. By using a combination of local and global explanation techniques, we can provide a comprehensive understanding of the decision-making process of these models, which can help to build trust in the model, identify biases, and improve model performance.
By following best practices and using a variety of techniques, we can effectively explain the predictions made by predictive models and improve their interpretability.
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