# Top 5 SAS Predictive Modeling Procedure – You Must Know

## 1. Objective

We looked at different types of analysis and the procedures used for performing it in the previous SAS/STAT tutorial, today we will be looking at another type of analysis, called SAS Predictive Modeling. In this tutorial, we will study introduction to Predictive Modeling with examples. Moreover, we will further discuss how can we use Predictive Modeling in SAS/STAT or the SAS Predictive Modeling Procedures: PROC PLS, PROC ADAPTIVEREG, PROC GLMSELECT, PROC HPGENSELECT, and PROC TRANSREG with examples & syntax.
So, let’s begin with SAS/STAT Predictive Modeling.

## 2. What is SAS Predictive Modeling?

Predictive modeling is a process that forecasts outcomes and probabilities through the use of data mining. In this, each model is made up of a specific number of predictors, which are variables that help in determining as well as influencing future results. Once all the data has been collected for the required number of relevant predictors, a statistical model is formulated.
In SAS Predictive modeling, the model is chosen on the basis of a detection theory that tries to guess the probability/possibility of an outcome given a specific amount of input data, say for example if given an email sent through predictive modeling, we determine how likely it is that it is spam.
Let’s Explore the important Features of SAS/STAT

## 3. Procedures for Predictive Modeling in SAS/STAT

Following procedures are used to compute SAS/STAT Predictive Modeling of a sample data. Let us explore it.

### a. PROC PLS

The PLS procedure in SAS/STAT is used to fit models through the use of linear predictive methods. Apart from fitting models, the techniques used in the PLS procedure have another goal of accounting for any variation in the predictors.
The techniques implemented using the PLS procedure are:

• Principal component regression technique, in which factors are extracted to explain the variation of predictor sample
• Reduced rank regression technique, in which factors are extracted to explain response variation.
• Partial least squares regression technique, where both response variation and predictor variation are accounted.

A Syntax of PROC PLS-

```PROC PLS DATASET<options>;
MODEL dependent-variables = effects </ options>;```

The PROC PLS and MODEL statements are required.
PROC PLS Example-

```proc pls data=SASHELP.iris plots=all;
model sepallength=sepalwidth|petalwidth;
run;```

Let’s revise SAS Mixed Model Procedures – PROC MIXED, PROC NLMIXED

Do you know about SAS/STAT Discriminant Analysis Procedure – PROC DISCRIM, CANDISC, STEPDISC

We have already discussed this procedure in SAS/STAT Non-Parametric Regression tutorial.

### c. PROC GLMSELECT

We have already discussed this procedure in SAS/STAT Model Selection tutorial.

### d. PROC HPGENSELECT

We have already discussed this procedure in SAS/STAT Model Selection tutorial.

### e. PROC TRANSREG

We have already discussed this procedure in SAS/STAT Market Research tutorial.

This was all about SAS Predictive Modeling Tutorial. Hope you like our explanation.

## 4. Conclusion

Hence, we learned Introduction to Predictive Modeling with an example. In conclusion, we saw different procedures used in SAS predictive modeling: PROC ADAPTIVEREG, PROC GLMSELECT, PROC HPGENSELECT, PROC TRANSREG, and PROC PLS with example & syntax. If you have any query, feel free to ask in the comment section.

Related Topic- SAS/STAT Categorical Data Analysis Procedure

For reference