{"id":6217,"date":"2025-03-09T20:19:07","date_gmt":"2025-03-09T16:19:07","guid":{"rendered":"https:\/\/eoninvestment.com\/?p=6217"},"modified":"2026-09-04T23:32:24","modified_gmt":"2026-09-04T19:32:24","slug":"logistic-regression","status":"publish","type":"post","link":"https:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/","title":{"rendered":"<span class=\"eon-recovered-georgian-title\" data-no-translation=\"\">\u10da\u10dd\u10ef\u10d8\u10e1\u10e2\u10d8\u10d9\u10e3\u10e0\u10d8 \u10e0\u10d4\u10d2\u10e0\u10d4\u10e1\u10d8\u10d0<\/span>"},"content":{"rendered":"<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Every important decision boils down to a &#8220;yes&#8221; or &#8220;no&#8221; choice \u2013 should I buy, or not buy? Should I offer a payment plan, or not? Should I agree, or not agree?<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The logistic regression model tries to predict the probability of an event happening by analyzing past data, using a 1 or 0 outcome in terms of &#8220;yes&#8221; or &#8220;no.&#8221; To understand how practical this method is, here are a few examples:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Customer Retention Strategy<\/strong> \u2013 Based on historical data, the model can predict the likelihood of a specific customer leaving, allowing us to focus on that segment.<\/li>\n\n\n\n<li><strong>Credit\/Loan Approval<\/strong> \u2013 The model can assess the probability of a customer defaulting on a loan\/credit, and recommend whether to grant the loan or not.<\/li>\n\n\n\n<li><strong>Fraud Diagnosis<\/strong> \u2013 A credit card company can detect the likelihood of fraudulent transactions based on card usage statistics.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The model\u2019s formula looks like this (Excel file at the end of the article):<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"771\" height=\"307\" data-attachment-id=\"6225\" data-permalink=\"https:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/image-387\/\" data-orig-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image.png?fit=771%2C307&amp;ssl=1\" data-orig-size=\"771,307\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image.png?fit=771%2C307&amp;ssl=1\" class=\"wp-image-6225\" style=\"width: 500px\" src=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image.png?resize=771%2C307&#038;ssl=1\" alt=\"\"\/><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It may look complicated, but the essence is that a combination of features is related to the probability of &#8220;yes&#8221; or &#8220;no&#8221; via a linear equation. It will be easy to understand with an example:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Let\u2019s assume I want to predict whether a debtor will pay their debt or default, based on historical data.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">First, I need the history \u2013 data about debtors and actual statistics on which ones failed to pay their debts. Below is a table with 5 parameters:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Loan Amount<\/li>\n\n\n\n<li>Annual Income<\/li>\n\n\n\n<li>Credit Rating<\/li>\n\n\n\n<li>Current Delinquency<\/li>\n\n\n\n<li>Previous Period Defaults<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The last column shows whether the customer actually defaulted, i.e., whether they were unable to pay their debt.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The data that the model receives can be anything, as long as it is logically related to the target probability of the outcome.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"651\" height=\"400\" data-attachment-id=\"6233\" data-permalink=\"https:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/image-390\/\" data-orig-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-3.png?fit=651%2C400&amp;ssl=1\" data-orig-size=\"651,400\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-3.png?fit=651%2C400&amp;ssl=1\" class=\"wp-image-6233\" style=\"width: 500px\" src=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-3.png?resize=651%2C400&#038;ssl=1\" alt=\"\"\/><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The next step involves building formulas for the Solver function (details will not be covered here):<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"1165\" height=\"347\" data-attachment-id=\"6238\" data-permalink=\"https:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/image-391\/\" data-orig-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-4.png?fit=1165%2C347&amp;ssl=1\" data-orig-size=\"1165,347\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-4.png?fit=1024%2C305&amp;ssl=1\" class=\"wp-image-6238\" style=\"width: 1000px\" src=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-4.png?resize=1165%2C347&#038;ssl=1\" alt=\"\"\/><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Solver must arrange the data in the yellow column in such a way that the logistic probability reaches its maximum, which will ultimately provide predictions in the form of 1\u2019s and 0\u2019s, or &#8220;yes&#8221; and &#8220;no.&#8221; The upper right corner shows the probability (Cut-off) coefficient, which determines whether the customer falls into the &#8220;yes&#8221; or &#8220;no&#8221; category. In simple terms \u2013 it is stated that if the default (Cut-off) probability is above 0.6, then place &#8220;yes&#8221; in the cell, otherwise &#8220;no.&#8221;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The next step is comparing the predictions made by the model to the reality:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"441\" height=\"149\" data-attachment-id=\"6243\" data-permalink=\"https:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/image-392\/\" data-orig-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-5.png?fit=441%2C149&amp;ssl=1\" data-orig-size=\"441,149\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-5.png?fit=441%2C149&amp;ssl=1\" class=\"wp-image-6243\" style=\"width: 500px\" src=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-5.png?resize=441%2C149&#038;ssl=1\" alt=\"\"\/><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We will calculate coefficients such as:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Accuracy<\/strong> \u2013 the proportion of correct predictions in the total statistics \u2013 (30+459)\/500;<\/li>\n\n\n\n<li><strong>Precision<\/strong> \u2013 the proportion of true positives among predicted positives \u2013 459\/462;<\/li>\n\n\n\n<li><strong>Recall<\/strong> \u2013 the proportion of of true positives among actual positives \u2013 459\/467;<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Based on this data, we generate sensitivity analysis graphs that help us choose the optimal Cut-off probability level:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"920\" height=\"398\" data-attachment-id=\"6248\" data-permalink=\"https:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/image-394\/\" data-orig-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-7.png?fit=920%2C398&amp;ssl=1\" data-orig-size=\"920,398\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-7.png?fit=920%2C398&amp;ssl=1\" class=\"wp-image-6248\" style=\"width: 700px\" src=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-7.png?resize=920%2C398&#038;ssl=1\" alt=\"\"\/><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For example, here, the curves intersect at a certain point, indicating a good level for the model (0.5).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Then, the Receiver Operating Characteristic (ROC) curve is built based on False Positive and True Positive statistics:<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"956\" height=\"392\" data-attachment-id=\"6249\" data-permalink=\"https:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/image-395\/\" data-orig-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-8.png?fit=956%2C392&amp;ssl=1\" data-orig-size=\"956,392\" data-comments-opened=\"1\" data-image-title=\"image\" data-image-description=\"\" data-image-caption=\"\" data-large-file=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-8.png?fit=956%2C392&amp;ssl=1\" class=\"wp-image-6249\" style=\"width: 700px\" src=\"https:\/\/i0.wp.com\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/image-8.png?resize=956%2C392&#038;ssl=1\" alt=\"\"\/><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This curve also helps us determine the right Cut-off level \u2013 we need to select a level where False Positive is minimized, and True Positive is maximized \u2013 in our case, it\u2019s 0.55. This means that if the model shows a default probability above 0.55, the loan should not be given, and vice versa.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>P.S.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Such calculations are usually done in specialized programs, Excel is not ideal for complex analysis, but it is often sufficient.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong><a href=\"https:\/\/eoninvestment.com\/wp-content\/uploads\/2025\/03\/17.1-logostic-regression.xlsx\">Logistic Regression Excel File<\/a><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Source of Idea:<\/strong><br \/>Business Analytics: Data Analysis &#038; Decision Making by S. Albright, &#038; Wayne Winston.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>","protected":false},"excerpt":{"rendered":"<p>\u10d1\u10d8\u10d6\u10dc\u10d4\u10e1 \u10e1\u10d0\u10db\u10e7\u10d0\u10e0\u10dd\u10e8\u10d8 \u10e4\u10d0\u10e0\u10d7\u10dd\u10d3 \u10d2\u10d0\u10db\u10dd\u10e7\u10d4\u10dc\u10d4\u10d1\u10d0\u10d3\u10d8 \u10e2\u10d4\u10e5\u10dc\u10d8\u10d9\u10d0\u10d0 \u10da\u10dd\u10d2\u10d8\u10e1\u10e2\u10d8\u10d9\u10e3\u10e0\u10d8 \u10e0\u10d4\u10d2\u10e0\u10d4\u10e1\u10d8\u10d0, \u10e0\u10d0\u10d3\u10d2\u10d0\u10dc \u10d1\u10d4\u10d5\u10e0\u10d8 \u10db\u10dc\u10d8\u10e8\u10d5\u10dc\u10d4\u10da\u10dd\u10d5\u10d0\u10dc\u10d8 \u10d2\u10d0\u10d3\u10d0\u10ec\u10e7\u10d5\u10d4\u10e2\u10d8\u10da\u10d4\u10d1\u10d0 &#8220;\u10d9\u10d8&#8221; \u10d0\u10dc &#8220;\u10d0\u10e0\u10d0&#8221; \u10d0\u10e0\u10e9\u10d4\u10d5\u10d0\u10dc\u10d6\u10d4 \u10d3\u10d0\u10d8\u10e7\u10d5\u10d0\u10dc\u10d4\u10d1\u10d0 &#8211; \u10d5\u10d8\u10e7\u10d8\u10d3\u10dd, \u10d7\u10e3 \u10d0\u10e0 \u10d5\u10d8\u10e7\u10d8\u10d3\u10dd; \u10d2\u10d0\u10d5\u10ea\u10d4 \u10d2\u10d0\u10dc\u10d5\u10d0\u10d3\u10d4\u10d1\u10d8\u10d7, \u10d7\u10e3 \u10d0\u10e0\u10d0? \u10d3\u10d0\u10d5\u10d4\u10d7\u10d0\u10dc\u10ee\u10db\u10dd, \u10d7\u10e3 \u10d0\u10e0 \u10d3\u10d0\u10d5\u10d4\u10d7\u10d0\u10dc\u10ee\u10db\u10dd?&#8230; \u10da\u10d8\u10d2\u10d8\u10e1\u10e2\u10d8\u10d9\u10e3\u10e0\u10d8 \u10e0\u10d4\u10d2\u10e0\u10d4\u10e1\u10d8\u10d8\u10e1 \u10db\u10dd\u10d3\u10d4\u10da\u10d8 [\u2026]<\/p>","protected":false},"author":12998525,"featured_media":6249,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_wpcom_ai_launchpad_first_post":false,"_eon_insights_quality":"","_eon_insights_decision":"","_eon_insights_destination":"","_eon_insights_merge_group":"","_eon_insights_required_changes":"","_eon_insights_start_order":0,"_eon_featured_insight":0,"_eon_insights_map_version":"","_jetpack_hide_featured_image":false,"_jetpack_featured_image_duplicate_dismissed":0,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false},"categories":[778789172,31110612,37372],"tags":[11287,784445428,4383675,778789224],"eon_topic":[],"eon_learning_path":[],"eon_collection":[],"eon_level":[784445728],"eon_content_type":[],"eon_source":[784445723],"eon_idea":[784445690],"eon_framework":[],"class_list":["post-6217","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-analytics-data-analysis-decision-making-by-s-albright-wayne-winston","category-excel-models","category-risk","tag-agile","tag-business-analytics","tag-logistic-regression","tag-logistic-regression-excel-file","eon_level-advanced","eon_source-business-analytics-data-analysis-decision-making-by-s-albright-wayne-winston","eon_idea-risk"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Logistic Regression - Eon Investment<\/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:\/\/eoninvestment.com\/ka\/2025\/03\/09\/logistic-regression\/\" \/>\n<meta property=\"og:locale\" content=\"ka_GE\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Logistic Regression - Eon Investment\" \/>\n<meta property=\"og:description\" content=\"\u10d1\u10d8\u10d6\u10dc\u10d4\u10e1 \u10e1\u10d0\u10db\u10e7\u10d0\u10e0\u10dd\u10e8\u10d8 \u10e4\u10d0\u10e0\u10d7\u10dd\u10d3 \u10d2\u10d0\u10db\u10dd\u10e7\u10d4\u10dc\u10d4\u10d1\u10d0\u10d3\u10d8 \u10e2\u10d4\u10e5\u10dc\u10d8\u10d9\u10d0\u10d0 \u10da\u10dd\u10d2\u10d8\u10e1\u10e2\u10d8\u10d9\u10e3\u10e0\u10d8 \u10e0\u10d4\u10d2\u10e0\u10d4\u10e1\u10d8\u10d0, \u10e0\u10d0\u10d3\u10d2\u10d0\u10dc \u10d1\u10d4\u10d5\u10e0\u10d8 \u10db\u10dc\u10d8\u10e8\u10d5\u10dc\u10d4\u10da\u10dd\u10d5\u10d0\u10dc\u10d8 \u10d2\u10d0\u10d3\u10d0\u10ec\u10e7\u10d5\u10d4\u10e2\u10d8\u10da\u10d4\u10d1\u10d0 &quot;\u10d9\u10d8&quot; \u10d0\u10dc &quot;\u10d0\u10e0\u10d0&quot; \u10d0\u10e0\u10e9\u10d4\u10d5\u10d0\u10dc\u10d6\u10d4 \u10d3\u10d0\u10d8\u10e7\u10d5\u10d0\u10dc\u10d4\u10d1\u10d0 - \u10d5\u10d8\u10e7\u10d8\u10d3\u10dd, \u10d7\u10e3 \u10d0\u10e0 \u10d5\u10d8\u10e7\u10d8\u10d3\u10dd; \u10d2\u10d0\u10d5\u10ea\u10d4 \u10d2\u10d0\u10dc\u10d5\u10d0\u10d3\u10d4\u10d1\u10d8\u10d7, \u10d7\u10e3 \u10d0\u10e0\u10d0? 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