By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data analytics in sports industry
    Here’s How Data Analytics In Sports Is Changing The Game
    6 Min Read
    data analytics on nursing career
    Advances in Data Analytics Are Rapidly Transforming Nursing
    8 Min Read
    data analytics reveals the benefits of MBA
    Data Analytics Technology Proves Benefits of an MBA
    9 Min Read
    data-driven image seo
    Data Analytics Helps Marketers Substantially Boost Image SEO
    8 Min Read
    construction analytics
    5 Benefits of Analytics to Manage Commercial Construction
    5 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: Prinicpal Components for Modeling
Share
Notification Show More
Latest News
big data mac performance
Data-Driven Tips to Optimize the Speed of Macs
News
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
Artificial Intelligence
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics
data analytics reveals the benefits of MBA
Data Analytics Technology Proves Benefits of an MBA
Analytics
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Predictive Analytics > Prinicpal Components for Modeling
Predictive Analytics

Prinicpal Components for Modeling

Editor SDC
Last updated: 2010/02/18 at 2:56 AM
Editor SDC
6 Min Read
SHARE

Problem Statement

Analysts constructing predictive models frequently encounter the need to reduce the size of the available data, both in terms of variables and observations. One reason is that data sets are now available which are far too large to be modeled directly in their entirety using contemporary hardware and software. Another reason is that some data elements (variables) have an associated cost. For instance, medical tests bring an economic and sometimes human cost, so it would be ideal to minimize their use if possible. Another problem is overfitting: Many modeling algorithms will eagerly consume however much data they are fed, but increasing the size of this data will eventually produce models of increased complexity without a corresponding increase in quality. Model deployment and maintenance, too, may be encumbered by extra model inputs, in terms of both execution time and required data preparation and storage.

Naturally, the goal in data reduction is to decrease the size of needed data…


Problem Statement

More Read

analyzing big data for its quality and value

Use this Strategic Approach to Maximize Your Data’s Value

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing
Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC
Quality Control Tips for Data Collection with Drone Surveying
3 Huge Reasons that Data Integrity is Absolutely Essential

Analysts constructing predictive models frequently encounter the need to reduce the size of the available data, both in terms of variables and observations. One reason is that data sets are now available which are far too large to be modeled directly in their entirety using contemporary hardware and software. Another reason is that some data elements (variables) have an associated cost. For instance, medical tests bring an economic and sometimes human cost, so it would be ideal to minimize their use if possible. Another problem is overfitting: Many modeling algorithms will eagerly consume however much data they are fed, but increasing the size of this data will eventually produce models of increased complexity without a corresponding increase in quality. Model deployment and maintenance, too, may be encumbered by extra model inputs, in terms of both execution time and required data preparation and storage.

Naturally, the goal in data reduction is to decrease the size of needed data, while maintaining (as much as is possible) model performance, this process must be performed carefully.

A Solution: Principal Components

Selection of candidate predictor variables to retain (or to eliminate) is the most obvious way to reduce the size of the data. If model performance is not to suffer, though, then some effective measure of each variable’s usefulness in the final model must be employed- which is complicated by the correlations among predictors. Several important procedures have been developed along these lines, such as forward selection, backward selection and stepwise selection.

Another possibility is principal components analysis (“PCA” to his friends), which is a procedure from multivariate statistics which yields a new set of variables (the same number as before), called the principal components. Conveniently, all of the principal components are simply linear functions of the original variables. As a side benefit, all of the principal components are completely uncorrelated. The technical details will not be presented here (see the reference, below), but suffice it to say that if 100 variables enter PCA, then 100 new variables (called the principal components come out. You are now wondering, perhaps, where the “data reduction” is? Simple: PCA constructs the new variables so that the first principal component exhibits the largest variance, the second principal component exhibits the second largest variance, and so on.

How well this works in practice depends completely on the data. In some cases, though, a large fraction of the total variance in the data can be compressed into a very small number of principal components. The data reduction comes when the analyst decides to retain only the first n principal components.

Note that PCA does not eliminate the need for the original variables: they are all still used in the calculation of the principal components, no matter how few of the principal components are retained. Also, statistical variance (which is what is concentrated by PCA) may not correspond perfectly to “predictive information”, although it is often a reasonable approximation.

Last Words

Many statistical and data mining software packages will perform PCA, and it is not difficult to write one’s own code. If you haven’t tried this technique before, I recommend it: It is truly impressive to see PCA squeeze 90% of the variance in a large data set into a handful of variables.

Note: Related terms from the engineering world: eigenanalysis, eigenvector and eigenfunction.

Reference

For the down-and-dirty technical details of PCA (with enough information to allow you to program PCA), see:

Multivariate Statistical Methods: A Primer, by Manly (ISBN: 0-412-28620-3)

Note: The first edition is adequate for coding PCA, and is at present much cheaper than the second or third editions.

TAGGED: data quality, data reduction, predictive modeling
Editor SDC February 18, 2010
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

big data mac performance
Data-Driven Tips to Optimize the Speed of Macs
News
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
3 Ways AI Has Helped Marketers and Creative Professionals Streamline Workflows
Artificial Intelligence
data analytics in sports industry
Here’s How Data Analytics In Sports Is Changing The Game
Big Data
data analytics on nursing career
Advances in Data Analytics Are Rapidly Transforming Nursing
Analytics

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

analyzing big data for its quality and value
Big Data

Use this Strategic Approach to Maximize Your Data’s Value

6 Min Read
data lineage tool
Big Data

7 Data Lineage Tool Tips For Preventing Human Error in Data Processing

6 Min Read
data quality and role of analytics
Data Quality

Preserving Data Quality is Critical for Leveraging Analytics with Amazon PPC

8 Min Read
data collection with drone use
Data Collection

Quality Control Tips for Data Collection with Drone Surveying

9 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 is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive

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?