Balancing Automation and Explanation in Machine Learning
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For a machine learning application to be successful, it is not enough to give highly accurate predictions: Customers also want to know why the model has made that prediction, so they can compare it against their intuition and (hopefully) gain trust in the model. However, there is a trade-off between model accuracy and explainability - for example, the more complex your feature transformations become, the harder it is to explain what the resulting features mean to the end customer. However, with the right system design this doesn't mean it has to be a binary choice between these two goals. It is possible to combine complex, even automatic, feature engineering with highly accurate models and explanations. We will describe how we are using lineage tracing to solve this issue at Salesforce Einstein, allowing good model explanations to coexist with automatic feature engineering and model selection. By building this into an open source AutoML library TransmogrifAI, an extension to SparkMlLib, it is easy to ensure a consistent level of transparency in all of our ML applications. As model explanations are provided out of the box, data scientists don't need to re-invent the wheel when model explanations need to be surfaced.
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Balancing Automation and Explanation in Machine Learning
- 1. Balancing Automation and Explanation in Machine Learning Leah McGuire, Till Bergmann
- 3. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmogrifAI on Spark How to explain your model
- 4. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmogrifAI on Spark How to explain your model
- 5. P1 (c | f) Pk (c | f) Pn (c | f) Σ Input Output
- 6. The Question Why did the machine learning model make the decision that it did? “
- 7. The best model or the model you can explain?
- 8. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmogrifAI on Spark How to explain your model
- 9. Keep it DRY (don’t repeat yourself) and DRO (don’t repeat others) https://transmogrif.ai/
- 10. Simple building blocks for automatic model generation // Automated feature engineering val featureVector = Seq(pClass, name, sex, age, sibSp, parch, ticket, cabin, embarked).transmogrify() // Automated feature selection val checkedFeatures = survived.sanityCheck(featureVector = featureVector, removeBadFeatures = true) // Automated model selection val prediction = BinaryClassificationModelSelector.setInput(survived, checkedFeatures).getOutput() // Model insights val model = new OpWorkflow().setInputDataset(passengersData).setResultFeatures(prediction).train() println("Model insights:n" + model.modelInsights(prediction).prettyPrint()) // Add individual prediction insights val predictionInsights= new RecordInsightsLOCO(model.getOriginStageOf(prediction)).setInput(pred).getOutput() val insights = new OpWorkflow().setInputDataset(passengersData).setResultFeatures(predictionInsights) .withModelStages(model).train().score()
- 11. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmogrifAI on Spark How to explain your model
- 12. Debuggability F1 Top contributing features for surviving the Titanic: 1. Gender 2. pClass 3. Body
- 14. Bias
- 15. Legal
- 16. Actionable
- 17. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmografAI on Spark How to explain your model
- 18. 24 Hours in the Life of Salesforce 1.5B emails sent 4M orders 260M social posts 3B Einstein predictions 888M commerce page views 44M reports and dashboards 41M case interactions 3M opportunities created 2M leads created 8M cases logged B2C Scale B2B Scale Source: Salesforce March 2018.
- 19. ETL Score and Update Models Deploy and Operationalize Models Model Training Feature Engineering Model Evaluation Data Cleansing The typical Machine Learning pipeline
- 20. Salesforce
- 21. We can’t build one global model •Privacy concerns • Customers don’t want data cross-pollinated •Business Use Cases • Industries are very different • Processes are different •Platform customization • Ability to create custom fields and objects •Scale, Automation • Ability to create
- 22. Multiply it by M*N (M = customers; N = use cases)
- 23. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmogrifAI on Spark How to explain your model
- 24. How to explain your model: Options: ● Feature weights / importance ● Model agnostic feature impact ● Secondary models ● Feature / label interactions ● Global versus local granularity Concerns: ● How interpretable is the model? ● How interpretable are the raw features? ● Do you care about explaining the model or gaining insights into data? ● Do we need to explain individual predictions?
- 26. Secondary Model
- 27. Feature Weight / Importance / Impact X X2 X3 X4 X5 Y 0 1 0 0 0 A 1 1 1 0 0 B 0 0 1 1 0 B 1 1 1 1 1 A 1 0 1 0 0 A
- 28. Global vs local explanations Titanic model top features: ● Gender ● Cabin class (pClass) ● Age Titanic passenger top features (ranking x value): ● Prediction = 1 (survived), Reasons = female, 1st Class ● Prediction = 0 (died), Reasons = male, 3rd Class
- 29. Feature Impact (LOCO) https://www.oreilly.com/ideas/ideas-on-interpreting-machine-learning Score = 0.27 Reasons => sex = "male" (-0.13), pClass = 3 (-0.05), ...
- 30. Issues with Feature Importance / Weight / Impact http://resources.esri.com/help/9.3/arcgisengine/java/gp_toolref/spatial_statistics_toolbox/multicollinearity.htm
- 31. A secondary model that tells you about your data https://www.statmethods.net/advgraphs/images/corrgram1.png
- 32. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmogrifAI on Spark How to explain your model
- 33. X1 X2 X3 X4 X5 Y 0 1 0 0 0 A 1 1 1 0 0 B 0 0 1 1 0 B 1 1 1 1 1 A 1 0 1 0 0 A Where did you get the feature matrix?
- 34. Making a DAG for feature engineering - and tracking it! val featureVector = Seq(pClass, name, gender, age, sibSp, parch, ticket, cabin, embarked).transmogrify() •Each feature is mapped to an appropriate .transmogrify() stage based on its type • gender (a Picklist) and age (an Integral) are automatically assigned to different stages •Metadata is updated with what happened at each step in transmogrification • age_bucket_0-10 (parentFeature = age, parentType = integral, grouping = age, value = 0-10) •Metadata is combined with feature importance measures to produce insights • age max contribution= -0.27
- 35. Use types to make feature engineering smart
- 36. Automated feature engineering DAG (with metadata!!) ZipcodeSubjectPhoneEmail Age Age [0-15] Age [15-35] Age [>35] Email Is Spammy Top 10 Email Domain Country Code Phone Is Valid Top TF-IDF Terms Average Income Vector ● The name of the RAW feature(s) the column was made from ● The name of the feature the column was made from ● Everything you did to get the column ● Any grouping information across columns ● Description of the value in the column
- 37. Combining the origin metadata with model interpretations Information we add: ● Correlation ● Mutual information ● Feature weight / importance ● Feature distribution description ● Feature contribution to each score (optionally)
- 38. Roadmap for this talk Automation vs Explanation Why explain your model Why automate your modeling How to automate with explanation in mind What does this look like Our solution: TransmogrifAI on Spark How to explain your model
- 39. Example input data case class Passenger ( id: Id, survived: RealNN, pClass: Integral, name: Text, sex: Picklist, age: Integral, sibSp: Integral, parCh: Integral, ticket: Id, fare: Currency, cabin: Picklist, embarked: Picklist )
- 40. Example Code // Automated feature engineering val featureVector = Seq(pClass, name, sex, age, sibSp, parch, ticket, cabin, embarked).transmogrify() // Automated feature selection val checkedFeatures = survived.sanityCheck(featureVector = featureVector, removeBadFeatures = true) // Automated model selection val prediction = BinaryClassificationModelSelector.setInput(survived, checkedFeatures).getOutput() // Model Insights val model = new OpWorkflow().setInputDataset(passengersData).setResultFeatures(prediction).train() println("Model insights:n" + model.modelInsights(prediction).prettyPrint()) // Add individual prediction insights val predictionInsights= new RecordInsightsLOCO(model.getOriginStageOf(prediction)).setInput(pred).getOutput() val insights = new OpWorkflow().setInputDataset(passengersData).setResultFeatures(predictionInsights) .withModelStages(model).train().score()
- 41. Example metadata case class OpVectorColumnHistory ( columnName: String, age_modeFill_bucketize_0-10 parentFeatureName: Seq[String], Seq(age_modeFill) parentFeatureOrigins: Seq[String], Seq(age) parentFeatureStages: Seq[String], Seq(modeFill, bucketize) parentFeatureType: Seq[String], Integral grouping: Option[String], Some(age_modeFill) indicatorValue: Option[String], Some(0-10) descriptorValue: Option[String], None index: Int 17 )
- 42. Example insights case class Insights ( derivedFeatureName: String, stagesApplied: Seq[String], derivedFeatureGroup: Option[String], derivedFeatureValue: Option[String], excluded: Option[Boolean], corr: Option[Double], cramersV: Option[Double], mutualInformation: Option[Double], pointwiseMutualInformation: Map[String, Double], countMatrix: Map[String, Double], contribution: Seq[Double], min: Option[Double], max: Option[Double], mean: Option[Double], variance: Option[Double] ) case class FeatureInsights ( featureName: String, featureType: String, derivedFeatures: Seq[Insights], distributions: Seq[FeatureDistribution], exclusionReasons: Seq[ExclusionReasons] )
- 43. What the customer sees:
- 44. So what is the point? ● The choice is not binary - you can have automation and explanation ● It takes a lot of work and tracking to get good explanations ● You can skip that and just use our solution :-)