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: Ten ways to build a wrong scoring model
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 > Big Data > Data Mining > Ten ways to build a wrong scoring model
Data MiningPredictive Analytics

Ten ways to build a wrong scoring model

Editor SDC
Last updated: 2009/03/12 at 4:09 PM
Editor SDC
3 Min Read
SHARE

Below are some ways to build a wrong scoring model. The author doesn’t make any guarantee that if your modeling team uses one of them they will still get a correct model.

1) Over-fit the model to the sample. This over-fitting can be checked by taking a random sample again and fitting the scoring equation and comparing predicted conversion rates versus actual conversion rates. The over-fit model does not rank order: deciles with lower average probability may show equal or more conversions than deciles with higher probability scores.

2) Choose non-random samples for building and validating the scoring equation. Read over-fitting above.

3) Use Multicollinearity without business judgment to remove variables that may make business sense. This usually happens a few years after you studied — and have now forgotten — multicollinearity… 

More Read

More Ways to get a Scoring Model wrong

Below are some ways to build a wrong scoring model. The author doesn’t make any guarantee that if your modeling team uses one of them they will still get a correct model.

1) Over-fit the model to the sample. This over-fitting can be checked by taking a random sample again and fitting the scoring equation and comparing predicted conversion rates versus actual conversion rates. The over-fit model does not rank order: deciles with lower average probability may show equal or more conversions than deciles with higher probability scores.

2) Choose non-random samples for building and validating the scoring equation. Read over-fitting above.

3) Use Multicollinearity without business judgment to remove variables that may make business sense. This usually happens a few years after you studied — and have now forgotten — multicollinearity.

If you don’t know the difference between Multicollinearity and Heteroscedasticity, this could be the real deal-breaker for you

4) Using legacy codes for running scoring, usually with step-wise forward and backward  regression. This usually happens on Fridays and when you’re in a hurry to make models.

5) Ignoring signs or magnitude of parameter estimates (that’s the output or the weightage of the variable in the equation).

6) Not knowing the difference between Type 1 and Type 2 errors, especially when rejecting variables based on P value.

7) Excessive zeal in removing variables. Why? Ask yourself this question every time you are removing a variable.

8) Using the wrong causal event (like mailings for loans) for predicting the future with scoring model (for mailings of deposit accounts). Or using the right causal event in the wrong environment (rapid decline/rise of sales due to factors not present in model like competitor entry/going out of business, oil prices, credit shocks sob sob sigh).

9) Over-fitting.

10) Learning about creating models from blogs and not  reading and refreshing your old statistics textbooks.

Share/Save/Bookmark

TAGGED: scoring models
Editor SDC March 12, 2009
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

More Ways to get a Scoring Model wrong

5 Min Read

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

giveaway chatbots
How To Get An Award Winning Giveaway Bot
Big Data Chatbots Exclusive
AI and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence 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?