{"id":110,"date":"2013-07-27T12:29:50","date_gmt":"2013-07-27T06:59:50","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=110"},"modified":"2017-04-04T13:54:00","modified_gmt":"2017-04-04T08:24:00","slug":"credit-scorecards-logistic-regression-part-5","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/credit-scorecards-logistic-regression-part-5\/","title":{"rendered":"Credit Scorecards – Logistic Regression (part 5 of 7)"},"content":{"rendered":"
\n

A Primer on Logistic Regression – Are you Happy?<\/span><\/h1>\n
\"Logistic<\/a>

Logistic regression for happiness- by Roopam<\/p><\/div>\n

A few years ago, my wife and I took a couple of weeks\u2019 vacation to England and Scotland. Just before boarding the British Airway\u2019s plane, an air-hostess informed us that we were upgraded to business class. Jolly good! What a wonderful start to the vacation. Once we got onto to the plane, we got another tempting offer for a further upgrade to the first class. However, this time, there was a catch \u2013 just one seat was available. Now that is a shame, of course, we could not take this offer. The business class seats were fabulous before the first class offer came \u2013 by the way, all free upgrades. This is the situation behavioral economist describe as relativity & anchoring \u2013 in plain English comparison. Anchoring or comparison is at the root of pricing strategies in business and also to all the human sorrow. However, eventually the vacation mood took over and we enjoyed the business class thoroughly. Humans are phenomenally good at adjusting to the situation in the end and enjoy it as well. You will find some of the happiest faces with people in the most difficult situations. Here is a quote by Henry Miller \u201cI have no money, no resources, no hopes. I am the happiest man alive<\/i>\u201d. Human behavior is full of anomaly \u2013 full of puzzles. The following is an example to strengthen this thesis.<\/p>\n

\"Source:<\/a>

Source: flicker.com<\/p><\/div>\n

Lennon, McCartney, Harrison, and Best are the members of the most famous band ever on the planet \u2013 the Beatles. Ok, I know you have spotted the error. By now your must have uttered out the right names: John Lennon, Paul McCartney, George Harrison and Ringo Starr not Pete Best. Actually, Ringo Starr was the replacement for Pete Best, the original regular drummer for the Beatles. Pete must have been devastated seeing his partners rising to glory while he was left behind. Wrong, search for him on Google – he is the happiest Beatle of all. Now that is counter intuitive, I guess we do not have a clue what makes us happy.<\/p>\n

As promised in a previous article, in this article I will attempt to explore happiness using logistic regression \u2013 the technique extensively used in scorecard development.<\/p>\n

Logistic Regression – An Experiment<\/span><\/h2>\n

I am a thorough empiricist \u2013 a proponent of fact-based management. Hence, let me design a quick and dirty experiment* to generate data to evaluate happiness. The idea is to identify the factors \/ variables that influence our overall happiness. Let me present a representative list of factors for a working adult living in a city:<\/p>\n

\"5<\/a><\/p>\n

Now, throw in some other factors to the above list such as \u2013 random act of kindness or an unplanned visit to a friend. As you could see, the above list can easily be expanded (recall the article on variable selection- article 3<\/a>). This is a representative list and you will have to create your own to figure out factors that influence your level of happiness.<\/p>\n

The second part of the experiment is to collect data. This is like maintaining a diary only this one will be in Microsoft Excel. Every night before sleeping, you could assess your day and fill up numbers in the Spreadsheet along with your overall level of happiness for the day (as shown in the figure below).<\/p>\n

\"5<\/a><\/p>\n

*I am calling this a quick and dirty experiment for the following reasons (1) It\u2019s not a well thought out experiment but is created more to illustrate how logistic regression works (2) the observer and the observed are same in this experiment which might create a challenge for objective measurement.<\/pre>\n
After a couple of years of data collection, you will have enough observations to create a model \u2013 a logistic regression model in this case. We are trying to model feeling of happiness (column B) with other columns (C to I) in the above data set. If we plot B on the Y-axis and the additive combination of C to I (we\u2019ll call it Z) on the X-axis it will look something like the plot shown below.<\/p>\n
\"5<\/a><\/p>\n
The idea behind logistic regression is to optimize Z in such a way that we get the best possible distinction between happy and sad faces, as achieved in the plot above. This is a curve-fitting problem with sigmoid function (the curve in violet) as the choice of function.<\/p>\n
I would recommend using dates of observations (column A) in our model; this might give an interesting influence of seasons on our mood.<\/p>\n
Applications in Banking and Finance<\/span><\/h2>\n
This is exactly what we do in case of analytical scorecards such as credit scorecards, behavioral scorecards, fraud scorecards or buying propensity models. Just replace happy and sad faces with \u2026<\/p>\n
\u2022 Good and Bad borrowers
\n\u2022 Fraud and genuine cases
\n\u2022 Buyers and non-buyers<\/p>\n
\u2026. for the respective cases and you have the model. If you remember in the previous article (4)<\/a>, I have shown a simple credit scorecard model:\u00a0Credit Score = Age + Loan to Value Ratio (LTV) + Instalment (EMI) to Income Ratio (IIR)<\/p>\n
A straightforward transformation of the sigmoid function will help us arrive at the above equation of the line. This is the final link to arrive at the desired scorecard.<\/p>\n
Variable Transformation in Credit Scorecards<\/span><\/h2>\n
\"The<\/a>
The Swordsmith – by Roopam<\/p><\/div>\n
I loved the movie Kill-Bill, both parts. In the first part, I enjoyed when Uma Thurman\u2019s character went to Japan to get a sword from Hattori Hanz\u014d, the legendary swordsmith. After learning about her motive, he agrees to make his finest sword for her. Then Quentin Tarantino, director of the movie, briefly showed the process of making the sword. Hattori Hanz\u014d transformed a regular piece of iron to the fabulous sword \u2013 what a craftsman. This is fairly similar to how analysts perform transformation of the sigmoid function to the linear equation. The difference is that analysts use mathematical tools rather than hammers and are not as legendary as Hattori Hanz\u014d.<\/p>\n
Reject Inference<\/span>
\n<\/span><\/h2>\n
Reject inference is a distinguishing aspect about credit or application scorecards which is different from all other classification models. For the application scorecards, the development sample is biased because of the absence of performance for rejected loans. Reject inference is a way to rectify this shortcoming and removing the bias from the sample. We will discuss reject inference in detail in some later article on YOU CANalytics.<\/p>\n
Sign-off Note<\/span><\/span><\/h4>\n
Now that we have our scorecard ready the next task is to validate the predictive power of the scorecard. This is precisely what we will do in the next article. See you soon.<\/p>\n
References<\/strong>\r\n1. Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring \u2013 Naeem Siddiqi<\/span>\r\n2. <\/span>Credit Scoring for Risk Managers: The Handbook for Lenders \u2013 Elizabeth Mays and Niall Lynas<\/span><\/pre>\n","protected":false},"excerpt":{"rendered":"
A Primer on Logistic Regression – Are you Happy? A few years ago, my wife and I took a couple of weeks\u2019 vacation to England and Scotland. Just before boarding the British Airway\u2019s plane, an air-hostess informed us that we were upgraded to business class. Jolly good! What a wonderful start to the vacation. Once<\/p>\n
Read More…<\/a><\/p>\n","protected":false},"author":1,"featured_media":113,"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 : Logistic Regression - 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-logistic-regression-part-5\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Credit Scorecards : Logistic Regression - YOU CANalytics\" \/>\n<meta property=\"og:description\" content=\"A Primer on Logistic Regression – Are you Happy? A few years ago, my wife and I took a couple of weeks\u2019 vacation to England and Scotland. Just before boarding the British Airway\u2019s plane, an air-hostess informed us that we were upgraded to business class. Jolly good! What a wonderful start to the vacation. 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Displayed below is the formula. $latex e^{i\\pi }+1=0 &s=2$ This formula is phenomenal because it is a combination of the five most\u2026","rel":"","context":"In "Banking Risk Case Study Example"","block_context":{"text":"Banking Risk Case Study Example","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/banking-risk-case-study-example\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/10\/The-Beautiful-Equation-Copy.jpg?fit=1200%2C879&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/10\/The-Beautiful-Equation-Copy.jpg?fit=1200%2C879&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/10\/The-Beautiful-Equation-Copy.jpg?fit=1200%2C879&ssl=1&resize=525%2C300 1.5x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/10\/The-Beautiful-Equation-Copy.jpg?fit=1200%2C879&ssl=1&resize=700%2C400 2x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2013\/10\/The-Beautiful-Equation-Copy.jpg?fit=1200%2C879&ssl=1&resize=1050%2C600 3x"},"classes":[]},{"id":3973,"url":"https:\/\/ucanalytics.com\/blogs\/model-selection-retail-case-study-example-part-7\/","url_meta":{"origin":110,"position":2},"title":"Model Selection – Retail Case Study Example (Part 7)","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Model Selection This is a continuation of our retail case study example for campaign and marketing analytics. In the previous two parts, we discussed a couple of decision tree algorithms (CART and C4.5)\u00a0for classification. 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The average Joe on the street was aware of terms such as mortgaged-backed securities (MBS), sub-prime\u2026","rel":"","context":"In "Credit Risk Analytics Series"","block_context":{"text":"Credit Risk Analytics Series","link":"https:\/\/ucanalytics.com\/blogs\/category\/risk-analytics\/credit-risk-analytics-series\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2015\/12\/Slide7.jpg?fit=290%2C210&ssl=1&resize=350%2C200","width":350,"height":200},"classes":[]},{"id":8107,"url":"https:\/\/ucanalytics.com\/blogs\/conversation-naeem-siddiqi-author-credit-risk-scorecards-credit-scoring-guru\/","url_meta":{"origin":110,"position":5},"title":"In Conversation with Naeem Siddiqi – Author Credit Risk Scorecards and Credit Scoring Guru","author":"Roopam Upadhyay","date":false,"format":false,"excerpt":"Predictive analytics is\u00a0considered one\u00a0of the sexiest professions\u00a0of our times. But where did it all start? What was the first business application of predictive analytics? It is hard to pin down one particular application but one of the earliest and highly successful applications is certainly credit risk models and retail credit\u2026","rel":"","context":"In "Events & Interviews"","block_context":{"text":"Events & Interviews","link":"https:\/\/ucanalytics.com\/blogs\/category\/events-and-interviews\/"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2016\/04\/Naeem-Siddiqi.jpg?fit=604%2C508&ssl=1&resize=350%2C200","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2016\/04\/Naeem-Siddiqi.jpg?fit=604%2C508&ssl=1&resize=350%2C200 1x, https:\/\/i0.wp.com\/ucanalytics.com\/blogs\/wp-content\/uploads\/2016\/04\/Naeem-Siddiqi.jpg?fit=604%2C508&ssl=1&resize=525%2C300 1.5x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/110"}],"collection":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/comments?post=110"}],"version-history":[{"count":0,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/posts\/110\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media\/113"}],"wp:attachment":[{"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/media?parent=110"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/categories?post=110"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/ucanalytics.com\/blogs\/wp-json\/wp\/v2\/tags?post=110"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}