The way LIME handles categorical variables is different from how it handles the continuous variables. The first 10 lines of the perturbed data are shown in the below illustrations to get an understanding of how the data changes at every stage. Leveraging this business scenario, now we will focus on the interpretation and understanding of how LIME explains predictions. Reference Data-Case 2 - Model comparison & XAI Reference Data-Case 1 - Data treatment & feature selection The complete details of the business case starting from masked raw data, data treatment approach, feature selection methods applied, model comparison, hyperparameter tuning and explainable AI framework, was converted into a solution and has been made available as data cases on our ‘Open Platform’ Analyttica TreasureHunt® LEAPS, below are the links – Complete XAI framework, built on LIME and SHAP techniques, for interpretation of risk score at a customer level The classification algorithm was further tuned to increase prediction accuracyĢ.ATH Auto ML feature was leveraged to compare the 5 models, and arrive at the final algorithm.We leveraged our patented platform Analyttica TreasureHunt® Precision Sandbox for rapid experimentation to test out 5 different algorithms for the prediction model.
Best model for predicting ‘Probability of Default’ of a customer over the next 6 months The objective here was to identify customers who are likely to default over the next 6 months, for which we developed a Risk Score to identify the risky customers and target them with remedial measures proactively.Īs part of the complete Risk scoring framework, the following elements were delivered to the business –ġ. One of our clients, a US based mid-sized Credit Card issuer, wanted to develop a data driven strategy to avoid customers from defaulting and implement remedial measures for the payment schedule. Feature weights (coefficients) of the simple model are the explanations of the observation.Fit a simple model to the perturbed data for the selected features.From the perturbed data, select m features that best describe the predictions.Calculate the distance from each perturbed data point to the original observation.
This perturbed data is a fake data created around the observation to be used by LIME to build the local linear model.
For the observation to be explained, perturb (unsettle or disturb) the observation n times to create replicated feature data with slight value modifications.LIME based XAI frameworks are one of the most popular techniques used, that work well with complex models. XAI provides the tools and techniques to interpret predictions. Such discrepancies in the model can be identified and fixed during the model development stage and with XAI, the analyst is better positioned to strengthen their model building process and boosting the reliability of their model. Being able to apply a ML model in a ‘White-Box’ approach, the Data Scientist can analyze the model at an atomic level and infer if the model is making the right predictions on the test data, though for the wrong reasons or if the model predictions are in line with the business domain and understanding. In the given scenario, where businesses are looking to leverage every piece of arsenal from their data assets, to find competitive advantage and be future ready to ride through this uncertainty, there is a growing need to be able to find solutions that are driven by application of complex ML techniques and at the same time being able to apply them in a “White Box” approach, providing rationale behind those predictions, which will enable, bringing all business stakeholders at the same page.Įxplainable AI not only helps the data scientist explain the model to the clients, business leads or the end users, however it also plays a key role in debugging a model. The complex ‘Machine Learning’ models are able to boost predictions, though their adoption has faced the hurdle of “Black-Box” implementation, where any insight on the workings of the model and especially the ability to explain predictions in a simplified format, have kept industry domains, especially the regulated ones like Financial Services and Healthcare, at bay, limiting their adoption to drive business impact. The tradeoff between the accuracy of a model and it’s explainability has always existed, though with the rise in popularity of complex ‘Machine Learning’ models and techniques, this trade off has risen to the fore front. Explainable AI (XAI) refers to methods and techniques in the application of artificial intelligence technology such that the results of the solution can be understood by human experts.
0 kommentar(er)
