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Binary Classification Use Case
Binary classification solutions target business outcomes with only two possible results, which are represented as text data.
Note Einstein Discovery stories are now models. We wish we could snap our fingers to update the
name everywhere, but you can expect to see the previous name in a few places until
we replace it.
Binary Outcomes
These outcomes are typically yes/no questions that are expressed in business terms, such as churned or not churned, opportunity won or lost, employee retained or not retained, and so on. For analysis purposes, Einstein Discovery converts the two values into boolean true and false.
Example Binary Classification Solutions
Here are just a few examples of how Einstein Discovery can help you improve binary classification outcomes in your organization:
- Predict the probability to win an Opportunity
- Predict the probability for an Account to buy a specific Product
- Predict the probability a Lead converts
- Predict the probability an Account churns
Model Template
Supported Algorithms for Binary Classification Solutions
- Generalized Linear Model (GLM) - Logistic Regression
- XGBoost
- Gradient Boost Machine (GBM)
- Random Forest
Model Quality Metrics
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