Explaining Machine Learning Models
Insights for Trustworthy Decision-Making
Machine learning models have often been regarded as “black boxes” due to their ability to make accurate predictions without providing a clear understanding of the underlying logic. However, the field of data explainability has emerged to address this challenge. By employing various techniques, data explainability models enable us to extract valuable insights from complex machine learning models. In this article, we explore the significance of data explainability insights through real-world applications.
1. Debugging and Error Identification
In real-life projects, dealing with unreliable and disorganized data is common. Explainability models facilitate the identification of discrepancies between the model’s findings and our understanding of the real world.
For example: Imagine a fraud detection model for an online marketplace. The explainability model reveals that the most important feature for fraud prediction is the customer’s shipping address. However, upon closer examination, it is discovered that the dataset used for training the model contained incorrect or outdated addresses. By identifying this pattern, the data team can address the issue, clean the data, and improve the model’s performance.
2. Informing feature engineering
Feature engineering is usually the most effective way to improve model accuracy. Sometimes you can go through this process using nothing but intuition about the underlying topic. But you’ll need more direction when you have 100s of raw features or when you lack background knowledge about the topic you are working on.
For example: In a healthcare setting, a model is developed to predict patient readmission rates. The explainability model highlights that the interaction between two features, age and a specific medical condition, significantly affects the predictions. This insight guides the data scientists to engineer a new feature that captures the interaction between age and the medical condition, resulting in a more accurate predictive model.
3. Directing future data collection
Model-based insights give you a good understanding of the value of features you currently have, which will help you reason about what new values may be most helpful.
For example: A customer churn prediction model for a subscription-based service reveals that usage frequency and customer complaints are the most influential features in predicting churn. Armed with this insight, the business can prioritize collecting more detailed data on customer complaints and usage patterns to gain a better understanding of churn drivers and develop proactive strategies to retain customers.
4. Informing human decision-making
Some decisions are made automatically by models. Amazon doesn’t have humans (or elves) scurry to decide what to show you whenever you go to their website. But many important decisions are made by humans. For these decisions, insights can be more valuable than predictions.
For example: In the context of loan approvals, an explainability model shows that a significant factor in the model’s decisions is the debt-to-income ratio. By providing a clear explanation of how this feature influences the loan approval process, loan officers can better understand the model’s recommendations and make more informed decisions when considering loan applications.
5. Building Trust
Many people won’t assume they can trust your model for important decisions without verifying some basic facts. This is a smart precaution given the frequency of data errors. In practice, showing insights that fit their general understanding of the problem will help build trust, even among people with little deep knowledge of data science.
For example: Suppose a credit scoring model is used by a financial institution to determine creditworthiness. The explainability model reveals that the model heavily relies on a customer’s credit history and payment behavior. By providing transparent explanations for the model’s decisions based on these factors, the financial institution can establish trust with customers and regulators, enabling them to make well-informed decisions and gain confidence in the model’s accuracy.
Conclusion:
Data explainability models offer invaluable insights that empower us to improve models, detect errors, guide decision-making, and build trust. By unraveling the inner workings of complex machine learning algorithms, we can harness the true potential of these models and make informed, trustworthy decisions.
