Training adaptive models
You can efficiently train adaptive models in bulk by using a data flow with a
dedicated decision strategy. This method can be useful if you have a historical data set
or simulated data and you want to perform further analysis, testing, or
simulations. Adaptive models do not require historical or sample data as the Adaptive Decision Manager
(ADM) typically starts without any data and quickly updates the models. However, you can
train your adaptive models with historical or sample customer responses. The use of
previous results enables the ADM server to create models that can predict behavior more
accurately. Adaptive models are Pega Platform scoring models that can capture
and analyze customer responses in real time. Adaptive models identify the most suitable
proposition for a customer or determine the proposition that the customer is most likely
to accept. The system creates adaptive models when you run a decision strategy that contains an
Adaptive Model component (strategy shape). For each proposition referenced in the
strategy, the system creates one adaptive model. You can view the models on the Model
Management landing page in Dev Studio. This tutorial explores the following use case: A company that serves a large number of customers wants to improve its method for
identifying the best proposition for each customer. The company can make several
propositions and has already acquired some information about its customers and
interaction data in an external database. The CustomerResponse database column contains
historical responses for a strategy in which each customer is presented with a group of
propositions and can choose only one (Insurance1, Insurance2, or Insurance3). When a
customer accepts a proposition, the system automatically rejects the other two. The company needs a solution that takes the customer interaction data, feeds the data to
adaptive models, and trains the models simultaneously. Adaptive models that are trained
with this information can determine with improved accuracy which proposition a customer
is most likely to accept. To support training adaptive models in bulk, you must create and configure two rules. The
first rule is your decision strategy that references the propositions that you want to offer to your
customers. This strategy must also reference an adaptive model rule that learns
from each recorded customer response. The second rule that you must create is a data
flow. The purpose of this data flow is to upload historical or sample data
from a dedicated data set into the adaptive model data set. First, you must create a strategy that references multiple propositions for
training adaptive models in bulk. This strategy must import proposition data and feed it
to an adaptive model that trains with each received response. For each proposition in the strategy, the system creates an adaptive model that you can
view on the Model Management landing page in Dev Studio. For
example, if your decision strategy references 50 propositions, the system creates 50
adaptive models. With complex expressions,
you can train adaptive models in scenarios where your database
contains multiple fields where customer responses are captured.
Consider the following expression: @if(
@contains(Primary.RESPONSE_POSITIVE, .pyName) , "Accept",
@if ( @contains(Primary.RESPONSE_NEGATIVE,
.pyName),"Decline", "None"). In this example,
the RESPONSE_POSITIVE database field contains positive customer
responses and the RESPONSE_NEGATIVE database field contains
negative customer responses. After you create your decision strategy, you must create a data flow that uploads
your historical or sample data to each adaptive model that was created on the Model
Management landing page. The source of the data flow is always the data set with
customer interaction data. The data flow must reference the decision strategy that trains the adaptive models that
you want to improve. The destination of the data flow is always the Adaptive Decision
Manager (ADM) server (pxAdaptiveAnalytics). If the class of the
data set and the decision strategy referenced in this data flow do not match, you must
convert the properties from the data set class to the strategy class.Sample database fragment with customer information and recorded response
GENDER LastName AGE FirstName INCOME MaritalStatus CustomerResponse F Hensley 66 Mary 165308 Married Insurance1 M Knightley 30 Robert 14663 Single Insurance3 F Cage 17 Susan 7250 Single Insurance1 F House 62 Margaret 21767 Married Insurance2 F Bryant 49 Anna 42517 Single Insurance3 Creating a strategy for training adaptive models in bulk
Creating a data flow for training adaptive models in bulk
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