Einstein Discovery - Evaluate, Deploy, and Manage Models Flashcards
What does the model produce in terms of types of insights?
Diagnostic, Predictions, Improvements
model
The sophisticated, custom equation that Einstein Discovery generates based on a comprehensive, statistical understanding of past outcomes. Einstein Discovery uses models to predict future outcomes. A model accepts the values of one or more predictor variables as input and produces a predicted outcome as output, along with (optionally) top factors and improvements. Einstein Discovery creates a model automatically whenever you create a new story version with Create Predictive Model enabled.
predictor
An explanatory variable that a model accepts as input in order to calculate a prediction. Predictors are also known as predictor variables or independent variables.
prediction
A derived value, produced by a model, that represents a possible future outcome. You can think of a prediction as the output of a predictive model that is based on the inputs of predictor variables that the model accepts.
top predictor
A condition that most significantly drives the predicted outcome. A condition is a data value associated with a variable. In Einstein Discovery, a predictor consists of one or two conditions.
improvement
A suggested action that a user can take to improve the likelihood of a desired outcome. Improvements are associated with actionable variables, which are variables over which users can possibly control or influence, such as the shipping method or a subscriber’s membership level. By taking the actions that Einstein suggests, users can increase their chances of having a more favorable outcome.
prediction definition
A container object in Einstein Discovery associated with one or more models. If a prediction definition contains multiple models, then each model produces predictions for a different segment of the data. A prediction definition can contain up to ten active models.
segmentation
Involves deploying models that target different segments (subsets) of your data. For example, suppose your data contains large, medium, and small customers, and your company organization is oriented around customer size to address the specialized needs of each group. You could build and deploy separate models for large, medium, and small customers to address the unique characteristics of each group. You define segments using filters that specify conditions for each group. Segmentation involves prediction definitions with multiple models.
prediction column
In a CRM Analytics dataset, the column where Einstein Discovery stores prediction values returned from the model.
3 types of models in ED
Numeric
Binary
Multiclass
How do you view model metrics?
Story toolbar -> model
