Predictive Modeling: Regressions and Neural Networks

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Table of Contents

Introduction
Working with Predictive Regression Models
Conclusion
Works Cited

Introduction

Predictive modeling is used to forecast results and outcomes for various types of situations and processes. Neural networks are the tools individuals can utilize for these purposes. This paper provides insight into the complex process of constructing predictive regression models, as well as training them and choosing appropriate input for modeling. In addition, the work offers an example of software that can be used for these purposes.

Working with Predictive Regression Models

Several factors should be considered during the construction of a predictive regression model. The process of development can be illustrated by the example of SAS software that is designed to perform this type of operation. Building regression predictive models requires identifying at least one classification or continuous variable. It is crucial to choose which variables will be inputted and the ones that will be rejected. In addition, an individual can create crossed effects by selecting at least two classifications, as well as a nested effect (SAS Enterprise Miner,). To make the predictive model work, a user should create a column for dependent variables as well.

It is necessary to mention that there are various data selection methods that can be used for a model. For example, the least angle regression method starts with no effects and adds them later; the estimated parameters are reduced and the classification variables are divided into groups (SAS Enterprise Miner,). Interpreting the data implies understanding the connection between variables and considering the p-value that suggests that the predictor and the response are not related.

A neural network is a parametric model that is flexible and can be trained according to individuals needs. For example, it is possible to use nodes that can utilize either a specific or several network configurations to identify the relationship in a data set (Analyze with a Neural Network). To train a neural network, an individual can use SAS software that enables it to have links between inputs and outputs, as well as the connection between hidden categories. It is necessary to mention that there are various training methods available, including those based on linear regression, loss function, and gradient descent. The choice of the approach should correspond to the purpose of the network and expected results.

Before choosing input for neural network predictive modeling, it may be necessary to ensure that inputs and output are normalized and the network is prepared for operations. For example, an individual may reduce the number of input variables to provide the high quality of the networks performance and results (SAS Enterprise Miner,). Moreover, the data should be divided into two parts, one of which will be utilized for training the neural network, and the other will serve as testing material. Then, it is vital to consider the link between the variables an individual is planning to select, as some of them may create the excessiveness of data, while others have no predictive value. A person should reflect on the benefits and disadvantages of each potential strategy compared to other ones. The selection should be primarily based on the problem and the expected output.

Conclusion

Constructing and interpreting regression predictive models, as well as choosing input for neural networks, can be challenging as there are many factors that should be considered. For example, it is vital to select appropriate variables to ensure that they have predictive value and can provide expected results. A correctly designed regression predictive model can be an effective tool that can be used to forecast outcomes for various types of research, as well as in daily life.

Works Cited

Analyze with a Neural Network Model. SAS, 2019. Web.

SAS Enterprise Miner: Impute, Transform, Regression & Neural Models. YouTube, uploaded by SAS Software, 2016, Web.

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Predictive Modeling: Regressions and Neural Networks

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