About the Rapid Predictive Modeler
Overview of the Rapid Predictive Modeler
SAS Rapid Predictive
Modeler is designed to build models for the following types of data
mining classification and regression problems:
-
classification models that predict the value of a discrete variable. Some examples are classification models that predict the value of a variable, such as True or False; Purchase or Decline; High, Medium, or Low; and Churn or Continues.
-
regression models that predict the value of a continuous variable. Some examples are regression models that might predict amounts such as revenue, sales, or success rate by using continuous values.
To create a model by
using the SAS Rapid Predictive Modeler, you must supply a data set,
where every row contains a set of independent predictor variables
(known as inputs) and at least one dependent variable (known as a
target). The SAS Rapid Predictive Modeler decides whether variables
are continuous or categorical, and chooses the input variables that
should be included in the model.
Your model can be saved
as SAS code and then deployed in a SAS environment. You can use the
SAS model code to score new data, and then use the results to make
more informed business decisions. This process is called model scoring.
For example, you can use scored data to decide which customers to
select for churn, or to detect transactions that might be fraudulent.
Sampling Strategies for the SAS Rapid Predictive Modeler
The SAS Rapid Predictive
Modeler uses a composite sampling approach. The number of observations
that are included in the data sample depend on these factors:
-
number of input variables
-
total number of observations in the data source
-
whether the data contains rare event targets
-
number of events in the data
Here are the guidelines
that the SAS Rapid Predictive Modeler uses to determine the number
of observations that are processed:
|
Number of Input Variables
|
Number of Observations
Processed
|
|---|---|
|
<100
|
80,000
|
|
100–200
|
40,000
|
|
>200
|
20,000
|
To understand the conditions
in the following table, here are some key points:
-
The number of observations being processed is determined by the number of input variables. See the preceding table.
-
In predictive modeling, if you are modeling a binary target, your target variable has an event level of 0 or 1. The event level could also be formatted to use No or Yes. Here is an example. A bank is trying to predict whether a customer will have bad credit. In the training data, each customer with bad credit is set to Yes, which means an event occurred for that customer. Each customer with good credit is considered a non-event.
|
Condition
|
Rare Event
|
|
|---|---|---|
|
Yes
|
No
|
|
|
total number of observations
< number of observations being processed
OR
total number of events
< (0.10*number of observations being processed)
|
Sample the data so that
there is a 10:1 ratio of non-events to events.
|
no sampling
|
|
total number of events
> (0.10*number of observations being processed)
|
Sample the following
proportion of the rare events:
 |
stratified sampling
|
Organizing Data for the SAS Rapid Predictive Modeler
Before you can build
a model, you need input data that represents historical events and
characteristics that can be used for prediction. You also need target
data that represents the event or value that you want to predict.
In many cases, the input data is derived from one time period and
the target data is derived from a later time period. The combined
input and target data that you use to develop your model is called training
data.
For example, you might
mine last year's sales receipts to predict next year's expected
revenue or to predict which customers will respond to a special offer.
Using historical data from past events to predict performance on future
events is called model training.
For the best model results,
your model training data should contain a large number of observations
stored as rows of data. For example, many retail customer models use
input data that has tens of thousands of observations.
If your target variable
contains a rare event (for example, an offer that perhaps only 1%
of your customers will respond to), you must ensure that your training
data contains a significant number of these customers in your data
set. You might want to oversample your training data to make sure
you select all customers who accepted the offer, and provide an equal
number of customers who did not accept. Oversampling makes it easier
for a model with a rare event target to find a stable solution.
When you perform oversampling
to boost rare event occurrences in your training data, you artificially
inflate the occurrence of targeted events in your training data relative
to the natural population. To compensate for the difference between
the training data and the population data, the SAS Rapid Predictive
Modeler provides you with a prior probability setting. Prior probability
settings specify the true proportional frequencies of the targeted
event in the population data.
The data that you mine
using the SAS Rapid Predictive Modeler should be organized into rows
(observations) and columns (variables). One of the columns should
represent a target variable.
Consider the following
example:
|
Name
|
Age
|
Gender
|
Income
|
Treatment
|
Purchase
|
|---|---|---|---|---|---|
|
Ricardo
|
29
|
M
|
33000
|
Y
|
Y
|
|
Susan
|
35
|
F
|
51000
|
Y
|
N
|
|
Jeremy
|
49
|
M
|
110000
|
N
|
Y
|
Name
a column that contains
ID values for each observation. The SAS Rapid Predictive Modeler does
not process ID variable columns for analytical content.
Age, Gender, Income, and Treatment
input columns that
are used by the SAS Rapid Predictive Modeler.
Purchase
a target column.
When you configure your
table of input data, you can also designate a frequency column. The
values in the frequency column are nonnegative integers and must sum
to 1.
By using the Variables
to exclude from the model role, you can also select columns
that you want the SAS Rapid Predictive Modeler to ignore during your
analysis.
Training data always
requires input and target variable values. Data that you use for scoring
requires only input variable values; a target column is optional.
When the model is used to make predictions from new data, the target
column is not required. When the model is used to monitor effectiveness,
the target column is required. Data that you use for scoring also
typically includes an ID column.
Reserved Prefixes for Variables
SAS Enterprise Miner
uses several default prefixes for generated nodes. If one of the variables
in your input data uses any of these prefixes, you might see an error
in the SAS log. If any of the variables in your input data set use
these prefixes, it is recommended that you change the name of the
variable in the input data set.
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