By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: How to Improve Predictive Accuracy? (Part 1)
Share
Notification Show More
Latest News
ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > How to Improve Predictive Accuracy? (Part 1)
Business IntelligenceData MiningPredictive Analytics

How to Improve Predictive Accuracy? (Part 1)

Editor SDC
Last updated: 2010/05/18 at 1:31 PM
Editor SDC
6 Min Read
SHARE
“Prediction is difficult, especially about the future.” – Yogi Berra (a baseball catcher)

One model or multiple models?

Several articles and blog posts have been written on Predictive Analytics and its role in improving business processes, reducing operational costs, increasing revenue among other things (see for example Eric Siegel’s article on Predictive Analytics with Data Mining: How It Works).  In spite of its widespread use and popularity, we often hear, “Past performance is no guarantee for future results…”   Obviously, a question arises naturally – How to improve predictive accuracy and hence make it more reliable?  This post discusses one such possible solution.

Contents
One model or multiple models? One model or multiple models?Bootstrap Aggregating (Bagging)

To understand the logic behind the solution, consider the following scenario: Suppose your Business Intelligence software has developed a suitable regression model to forecast Sales Volume for the next quarter after following all the steps of the model building process scrupulously.  Further, suppose that the model is validated by employing one of the standard model validation procedures such as Cross-validation or bootstrap.  Now your model (“Expert”) is ready for deployment. …

“Prediction is difficult, especially about the future.” – Yogi Berra (a baseball catcher)

More Read

predictive analytics in dropshipping

Predictive Analytics Helps New Dropshipping Businesses Thrive

Embedded BI Tools Bring Huge Benefits to Business Applications
Data Mining Technology Helps Online Brands Optimize Their Branding
Promising Benefits of Predictive Analytics in Asset Management
What Role Does Big Data Have on the Deep Web?

One model or multiple models?






Several articles and blog posts have been written on Predictive Analytics and its role in improving business processes, reducing operational costs, increasing revenue among other things (see for example Eric Siegel’s article on Predictive Analytics with Data Mining: How It Works).  In spite of its widespread use and popularity, we often hear, “Past performance is no guarantee for future results…”   Obviously, a question arises naturally – How to improve predictive accuracy and hence make it more reliable?  This post discusses one such possible solution.

To understand the logic behind the solution, consider the following scenario: Suppose your Business Intelligence software has developed a suitable regression model to forecast Sales Volume for the next quarter after following all the steps of the model building process scrupulously.  Further, suppose that the model is validated by employing one of the standard model validation procedures such as Cross-validation or bootstrap.  Now your model (“Expert”) is ready for deployment.

Let us compare the above strategy with a real-life scenario:  When the Board of Directors has to take a critical decision, several experts are consulted instead of just one.  If that is the case, then when a critical futuristic revenue generation or cost cutting plan has to be launched, why should we not think of using multiple models to base our decision upon instead of just concentrating on one as planned above?   Precisely, we are going to do this here.

Bootstrap Aggregating (Bagging)






This technique was initially proposed by Breiman (1996) to improve the predictive reliability of Decision Trees.  Bagging and Boosting are the two strategies that are used to increase the predictive accuracy.  In this post we will discuss the Bagging technique.

Traditionally, a predictive model – say a regression model or a Decision Tree is developed using a given training set D.  In the Bagging method, D is split into some smaller sets of samples Di of the same size as that of D (i = 1, 2, 3….k; where, k = some suitable number).  These sets are selected by generating random samples with replacement, called Bootstrap sampling from the original set D.  Based on each bootstrap sample, a predictive model is developed.  With this, you will get an ensemble of k models as shown in the figure below:

If your goal is to predict the values say Sales Volume for the next quarter, then the Bagging rule is to use each model Mi to predict future sales and finally obtain the average predicted value.  If your goal is to build a classifier- say to identify a churner or a loan-defaulter, then using each of the k models, classify a customer as a churner or loan-defaulter and base your final decision on ‘majority vote’.
The bagged prediction or classifier often has more accuracy than a single model or classifier based on the data D.  This happens because the aggregation process reduces the instability or variability present in a single model.  The following case illustrates the advantage of Bagging:


Sales Forecast

Let us fit a regression model to predict Sales Volume based on the amount spent on Advertizing.  After fitting is done, apply Bagging tool to obtain the predictions.  The above bar chart displays the Sales forecast before Bagging (blue bars) and after Bagging (green bars) process along with their confidence levels.  As you can see, Sales forecasts after Bagging are far more reliable.

In my next post, I will be discussing about the Boosting technique to improve predictive accuracy.

TAGGED: business intelligence, data mining, decision trees, predictive analytics
Editor SDC May 18, 2010
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

predictive analytics in dropshipping
Predictive Analytics

Predictive Analytics Helps New Dropshipping Businesses Thrive

12 Min Read
embedded bi tools
Business Intelligence

Embedded BI Tools Bring Huge Benefits to Business Applications

5 Min Read
data mining
Data Mining

Data Mining Technology Helps Online Brands Optimize Their Branding

7 Min Read
analyst,women,looking,at,kpi,data,on,computer,screen
Predictive Analytics

Promising Benefits of Predictive Analytics in Asset Management

11 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

AI chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
data-driven web design
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?