The importance of Explainable AI
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B2Metric is a Top-Notch Automated ML Modeling Platform For Banking And Finance. We use B2metric ML Studio for our Banking customer retention, segmentation, behavior analysis and propensity predictions. High-accurate ML models give us great customer insights...
With the B2ML Studio platform, you can easily integrate many different databases and get BI reporting, AutoML modeling, and explainable AI results on a single platform of your data.
Decision trees provide an explanation of the model's decision judgment by converting the results of the data distribution learned by modeling into simple rule sets.Thus, you can find out which situations are for or against your business model by following the rule sets.
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We have searched and tried many Auto-ML tools before. But B2Metric is the most suitable customer journey predictive analytics solution for our insurance business needs. Automated machine learning solutions with divergent thinking systems for churn prediction, risk analysis and customer segmentation in life insurance customers.
We built our weekly data analysis reports architecture with B2Metric. They have done an excellent job by providing a solution for our endless needs as an OTT startup.
B2Metric has successfully integrated and deployed a dashboard where we can continuosly track critical parameters for our business. Plus, agility in response, clear user interface, well designed data relation.
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Decision Tree is interpreted by following tree paths that reach nodes that separates the target class in a highly purely manner. To reach pure nodes, we find the nodes with the highest rate differences in bars.
- Colors are used to indicate the density of classes.
- B2M Main Node tells you what percentage of your target audience belongs to which class in classification problem and how many instance your data set consists of.
- B2M Child Node displays the density of target classes at a particular node in the decision tree by providing horizontal bar colored by target classes.
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Sunburst shows hierarchy through a series of rings, that are sliced for each category node. Each ring corresponds to a level in the hierarchy, with the central circle representing the root node and the hierarchy moving outwards from it. Rings are sliced up and divided based on their hierarchical relationship to the parent slice. The angle of each slice is either divided equally under its parent node or can be made proportional to a value. Colour can be used to highlight hierarchical groupings or specific categories.
The feature importance list is the list of variables that affect the results of this model, which best describes the underlying meaning of the data. By looking at the variables in this list, we can classify, prioritize and interpret the factors that affect our business cycle.
Feature Dependency & Shapley Importance
Feature dependency shows the list of variables that affect model decisions.
Feature Dependency & Shapley Importance
SHAP values are a convenient, (mostly) model-agnostic method of explaining a model’s output, or a feature’s impact on a model’s output. It provides a means to estimate and demonstrate how each feature’s contribution influences the model.
The regression coefficients are a statically measure which is used to measure the average functional relationship between variables. Also, it measures the degree of dependence of one variable on the other(s).
A decision surface plot is a powerful tool for understanding how a given model “sees” the prediction task. It also explains how the model has decided into a specific data segment. For example, given plot shows the model decision for Passenger Id <=23 data segment.
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