You’ve built a model. It performs well on your test data. But can you explain why it makes the predictions it does?

In the real world, performance alone isn’t enough. Stakeholders want to understand the logic. Regulators may require explanations. And you need to trust that your model is learning real patterns, not noise.

This is where interpretability comes in—and it’s fundamental to how Kanva works.

Why Understanding Predictions Matters

Building Trust

People are naturally skeptical of black boxes. When a model predicts a risk or recommends an action, users want to know why. Predictions they understand, they trust. Predictions they trust, they act on.

Catching Problems

Strong test performance doesn’t guarantee real-world success. Interpretability helps you catch:

  • Data leakage: Is the model using information it shouldn’t have access to?
  • Spurious correlations: Is it learning patterns that won’t generalize?
  • Bias: Are inappropriate factors influencing decisions?

The best way to catch these issues? Look at what the model learned.

Meeting Requirements

Many industries have regulatory requirements around explainable decisions:

  • Financial services (credit decisions)
  • Healthcare (diagnostic recommendations)
  • Insurance (risk assessments)
  • HR (hiring and evaluation)

Kanva’s built-in explanations help you meet these requirements.

Improving Models

Understanding what a model learned reveals opportunities:

  • Which features are actually valuable?
  • What patterns is it missing?
  • Where does it struggle?

This insight drives better feature engineering and model iteration.

How Kanva Provides Interpretability

Global Feature Importance

At the model level, Kanva shows you which features matter most overall:

  • Importance scores: How much each feature contributes to predictions
  • Ranking: Features sorted by predictive power
  • Visualization: Clear charts showing relative importance

This tells you what the model is “paying attention to” in aggregate.

Per-Prediction Explanations

For individual predictions, Kanva breaks down the contributing factors:

  • Which features mattered for this specific prediction?
  • How did each feature push the prediction up or down?
  • What was the baseline, and how did we get to this result?

This uses SHAP (SHapley Additive exPlanations)—a technique from game theory that produces consistent, additive explanations.

Visualization Options

Kanva presents interpretability through clear visualizations:

Summary plots show feature importance across your entire dataset with the distribution of impact.

Waterfall charts break down individual predictions, showing how each feature moved the prediction from the baseline.

Dependence plots reveal how a single feature affects predictions across its range of values.

Plain Language Summaries

Not everyone wants to parse charts. Kanva also generates natural language summaries:

“This prediction of HIGH RISK is primarily driven by:

  • Late payments in the last 6 months (strong increase in risk)
  • Low account balance (moderate increase in risk)
  • Long customer tenure (moderate decrease in risk)”

These summaries make explanations accessible to all stakeholders.

Interpretability in Practice

During Model Development

Use interpretability to validate your model makes sense:

  1. Train your model
  2. Review feature importance—do the top features align with domain expectations?
  3. Examine explanations for specific predictions—do they make sense?
  4. Investigate any surprises—they might reveal data issues or real insights

In Production

For deployed models, interpretability serves ongoing needs:

  • Audit trails: Log explanations alongside predictions
  • User trust: Show users why decisions were made
  • Monitoring: Track if explanation patterns shift over time

For Compliance

When regulators or auditors ask questions:

  • Show that inappropriate factors aren’t driving decisions
  • Demonstrate the logic behind individual decisions
  • Provide documentation of model behavior

The Interpretability-Performance Question

There’s a common belief that understanding requires sacrificing performance—using simple models instead of powerful ones.

This is largely outdated. Modern explanation techniques work with sophisticated models:

  • Gradient boosting
  • Random forests
  • Neural networks (with appropriate methods)

Kanva gives you the best of both worlds: powerful predictive models with clear explanations.

Understanding Enables Trust

Interpretability isn’t a nice-to-have—it’s essential for production use. With Kanva, you get explanations built into every model you build, not bolted on as an afterthought.

When stakeholders ask “why did the model predict that?"—you’ll have an answer. And that answer is what turns predictions into action.

Understanding your models means trusting your models. And trust is what turns predictions into business impact.

Want to see interpretability in action? Learn more about Kanva