{"id":48,"date":"2013-07-15T17:39:11","date_gmt":"2013-07-15T12:09:11","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=48"},"modified":"2017-05-03T12:07:09","modified_gmt":"2017-05-03T06:37:09","slug":"credit-scorecards-variables-selection-part-3","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/credit-scorecards-variables-selection-part-3\/","title":{"rendered":"Credit Scorecards – Variables Selection (part 3 of 7)"},"content":{"rendered":"
\n

Variables Selection in Predictive Analytics<\/span><\/h2>\n
\"Predictive<\/a>

Predictive Analytics: Variables Selection – by Roopam<\/p><\/div>\n

The following story goes back to the time when I just started my transition from physics to business. I met this investment banker* in his mid-thirties during a Friday night party. After gulping down a few pints of beer, his mood became a bit somber and he told me how he hates his job. However, he had a plan of working his ass off until he retires at 45. Then he will do everything that makes him happy. I was thoroughly confused, how could someone debar himself from an emotion \u2013 happiness \u2013 for so many years and rediscover it later? I was wondering about the recipe for happiness \u2013 raindrops on roses and whiskers on kittens<\/em>. An individual\u2019s happiness is a tricky thing; however, I shall attempt to tackle this issue in my later article on logistic regression. For now, let us try to explore how states measure the collective well-being of their people. I shall use this topic of population well-being to explore an interesting topic in analytical scorecard development: variables selection.<\/p>\n

Variables Selection – Lessons from GDP & GNH<\/span><\/h2>\n

The most popular measure for national prosperity, unanimously projected by economists and TV channels, is Gross Domestic Product (GDP). The equation for measuring GDP as taught in macroeconomics 101 is:<\/p>\n

\"GDP<\/a><\/p>\n

Clearly, there are 5 factors\/variables that govern GDP according to this equation. The first look at GDP as a measure for national well-being seemed incomplete to me. All the variables for GDP were from commerce. They are important but cannot be the only factors for country\u2019s well-being, more so in a highly diverse & complicated country like India.<\/p>\n

Gross National Happiness Index – The Story of Bhutan Naresh<\/span><\/h2>\n
\"\"<\/a>

Variables Selection – by Roopam<\/p><\/div>\n

Ok, so what else do we have? A lesser-known index is Gross National Happiness (GNH). The origins of GNH are in Bhutan. They measure their country\u2019s progress through GNH. The term was coined and implemented by Jigme Singye Wangchuck. This name immediately takes me back to the early nineties live telecast of the SAARC summit by India\u2019s national broadcaster Doordarshan (DD). The old-timer Hindi commentators were referring to a modest man in a bathrobe-like-attire as \u2018Bhutan Naresh\u2019 \u2013 King of Bhutan. At first glance, he did not fit well with the power horses of the south Asian region. Nevertheless, he seems to have devised a more holistic metric to\u00a0measure his country\u2019s well-being. GNH is a combination of the following broad categories:<\/p>\n

1. Living standard & income
\n2. Health coverage
\n3. Physiological well-being
\n4. Time spent at work and relaxing
\n5. Good governance
\n6. Schooling & education
\n7. Cultural diversity
\n8. Community vitality
\n9. Environmentalism and conservatism<\/p>\n

There are 72 total variables in GNH measured on a scale of 0 to 1, such as daily hours of sleep and trust in media; hmmm, not a bad start! You could do your own research on GNH and let me know what you feel about it. Actually, we can work out our own formula for a GNH like metric. The idea is to select the right variables to build your model!<\/p>\n

Variables Selection in Credit Scoring<\/span><\/h2>\n

In data mining and statistical model building exercises, similar to credit scoring, variables selection process is performed through statistical significance \u2013 a reasonably automated process through advanced software. However, the variables are still created and measured by humans. High impact analyses in businesses are still driven by hunches. Human intelligence is not obsolete yet.<\/p>\n

In one of the projects I did with a financial organization, the result of credit risk analysis and scoring led to redesigning of the application form. Application forms are a major source of data collection regarding the borrower. However, nobody wants to fill a lengthy form hence an optimal size of the form ensures accurate information provided by the borrower. The idea is to select the right variable and ensure accurate measurement.<\/p>\n

There are several aspects regarding variables but I will mention just one of them here (coarse classing).<\/p>\n

Coarse Classing in Credit Scoring<\/span><\/h2>\n

\"3<\/a>One of my favorite activities as a kid was going to a shoe store and getting my feet measured every summer before the school started. The shoe shops had a strange, miniature, slide-like device to measure foot size. It was fun to see my feet grow from one size to another every year or two. The growth was quantized i.e you are size-2 or 3 never 2.5 or 2.7. This aspect of converting measure such as 2.5 & 2.7 to 3 is called grouping, bucketing or classing. This is an integral part of creating scorecards that you will find in all the books I have listed in the first part of this blog series.<\/p>\n

I have been a part of several heated discussions on the relevance of coarse class in scorecard development throughout my career. In most, if not all academic articles you will rarely see coarse classing as a technique during model development. Quite a few academicians & practitioners for a good reason believe that coarse classing results in loss of information. However, in my opinion, coarse classing has the following advantage over using raw measurement for a variable.<\/p>\n

1. It reduces random noise that exists in raw variables \u2013 similar to averaging and yes, you lose some information here.
\n2. It handles extreme events \u2013 on two extremes of a variable \u2013 much better where you have thin data.
\n3. It handles the non-linear relationship between dependent and independent variable without a lot of effort of variable transformation from the analyst.<\/p>\n

Sign-off Note\u00a0<\/span><\/h4>\n

We are half way through this series on \u2018Analytical Scorecard Development\u2019 and I am enjoying writing this thoroughly. I hope as a reader you are on the same page. Scorecard building is highly technical and I have tried to discuss some aspects with easy to understand examples. However, to manage the length of the article, I am not able to get into the details. I must say that I love the details! So, if you have any queries, doubts, points-of-view or recommendations please write back on the discussion board or on my email: roopam.up@gmail.com<\/p>\n

*I do not remember correctly if he was an investment banker but it fits the description better \u2013 I should also not refrain from treating the community like a punching bag (going by the popular emotions).<\/pre>\n","protected":false},"excerpt":{"rendered":"
Variables Selection in Predictive Analytics The following story goes back to the time when I just started my transition from physics to business. I met this investment banker* in his mid-thirties during a Friday night party. After gulping down a few pints of beer, his mood became a bit somber and he told me how<\/p>\n
Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":49,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[55,54],"tags":[8,7,69,6,10],"jetpack_publicize_connections":[],"yoast_head":"\nCredit Scorecards : Variables Selection - YOU CANalytics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/ucanalytics.com\/blogs\/credit-scorecards-variables-selection-part-3\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Credit Scorecards : Variables Selection - YOU CANalytics\" \/>\n<meta property=\"og:description\" content=\"Variables Selection in Predictive Analytics The following story goes back to the time when I just started my transition from physics to business. 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Today we are starting a new series on YOU CANalytics called 'in conversation with'. In this series we will talk to the leaders and experts of predictive analytics and big data to gain deeper insight into the field. 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