{"id":829,"date":"2023-07-28T14:53:35","date_gmt":"2023-07-28T14:53:35","guid":{"rendered":"https:\/\/statorials.org\/ko\/mahalanobis-%e1%84%8b%e1%85%af%e1%86%ab%e1%84%80%e1%85%a7%e1%86%a8-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/"},"modified":"2023-07-28T14:53:35","modified_gmt":"2023-07-28T14:53:35","slug":"mahalanobis-%e1%84%8b%e1%85%af%e1%86%ab%e1%84%80%e1%85%a7%e1%86%a8-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/mahalanobis-%e1%84%8b%e1%85%af%e1%86%ab%e1%84%80%e1%85%a7%e1%86%a8-%e1%84%91%e1%85%a1%e1%84%8b%e1%85%b5%e1%84%8a%e1%85%a5%e1%86%ab\/","title":{"rendered":"Python\uc5d0\uc11c mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\"><strong>\ub9c8\ud560\ub77c\ub178\ube44\uc2a4 \uac70\ub9ac\ub294<\/strong> \ub2e4\ubcc0\ub7c9 \uacf5\uac04\uc5d0\uc11c \ub450 \uc810 \uc0ac\uc774\uc758 \uac70\ub9ac\uc785\ub2c8\ub2e4. \uc774\ub294 \uc5ec\ub7ec \ubcc0\uc218\ub97c \ud3ec\ud568\ud558\ub294 \ud1b5\uacc4 \ubd84\uc11d\uc5d0\uc11c \uc774\uc0c1\uac12\uc744 \ud0d0\uc9c0\ud558\ub294 \ub370 \uc790\uc8fc \uc0ac\uc6a9\ub429\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4.<\/span><\/p>\n<h3> <span style=\"color: #000000;\"><strong>\uc608: Python\uc758 Mahalanobis \uac70\ub9ac<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">Python\uc758 \ub370\uc774\ud130\uc138\ud2b8\uc5d0 \uc788\ub294 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud55c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub824\uba74 \ub2e4\uc74c \ub2e8\uacc4\ub97c \uc0ac\uc6a9\ud558\uc138\uc694.<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1\ub2e8\uacc4: \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uba3c\uc800, 20\uba85\uc758 \ud559\uc0dd\uc758 \uc2dc\ud5d8 \uc810\uc218\uc640 \ud568\uaed8 \uacf5\ubd80\ud55c \uc2dc\uac04, \uce58\ub978 \uc5f0\uc2b5 \uc2dc\ud5d8 \ud69f\uc218, \ucf54\uc2a4\uc758 \ud604\uc7ac \uc131\uc801\uc744 \ud45c\uc2dc\ud558\ub294 \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub9cc\ub4ed\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">import<\/span> numpy <span style=\"color: #107d3f;\">as<\/span> np\n<span style=\"color: #107d3f;\">import<\/span> pandas <span style=\"color: #107d3f;\">as<\/span> pd<span style=\"color: #107d3f;\">\nimport<\/span> scipy <span style=\"color: #107d3f;\">as<\/span> stats\n\ndata = {'score': [91, 93, 72, 87, 86, 73, 68, 87, 78, 99, 95, 76, 84, 96, 76, 80, 83, 84, 73, 74],\n        'hours': [16, 6, 3, 1, 2, 3, 2, 5, 2, 5, 2, 3, 4, 3, 3, 3, 4, 3, 4, 4],\n        'prep': [3, 4, 0, 3, 4, 0, 1, 2, 1, 2, 3, 3, 3, 2, 2, 2, 3, 3, 2, 2],\n        'grade': [70, 88, 80, 83, 88, 84, 78, 94, 90, 93, 89, 82, 95, 94, 81, 93, 93, 90, 89, 89]\n        }\n\ndf = pd.DataFrame(data,columns=['score', 'hours', 'prep','grade'])\ndf.head()\n\n score hours prep grade\n0 91 16 3 70\n1 93 6 4 88\n2 72 3 0 80\n3 87 1 3 83\n4 86 2 4 88\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>2\ub2e8\uacc4: \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud55c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\ub2e4\uc74c\uc73c\ub85c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \uac04\ub2e8\ud55c \ud568\uc218\ub97c \uc791\uc131\ud558\uaca0\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\"><span style=\"color: #008080;\">#create function to calculate Mahalanobis distance<\/span>\ndef<\/span> mahalanobis(x= <span style=\"color: #107d3f;\">None<\/span> , data= <span style=\"color: #107d3f;\">None<\/span> , cov= <span style=\"color: #107d3f;\">None<\/span> ):\n\n    x_mu = x - np.mean(data)\n    if not cov:\n        cov = np.cov(data.values.T)\n    inv_covmat = np.linalg.inv(cov)\n    left = np.dot(x_mu, inv_covmat)\n    mahal = np.dot(left, x_mu.T)\n    return mahal.diagonal()\n\n<span style=\"color: #107d3f;\"><span style=\"color: #008080;\">#create new column in dataframe that contains Mahalanobis distance for each row<\/span><\/span>\ndf['mahalanobis'] = mahalanobis(x=df, data=df[['score', 'hours', 'prep', 'grade']])\n\n<span style=\"color: #008080;\">#display first five rows of dataframe\n<\/span>df.head()\n\n score hours prep grade mahalanobis\n0 91 16 3 70 16.501963\n1 93 6 4 88 2.639286\n2 72 3 0 80 4.850797\n3 87 1 3 83 5.201261\n4 86 2 4 88 3.828734\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\"><strong>3\ub2e8\uacc4: \uac01 Mahalanobis \uac70\ub9ac\uc5d0 \ub300\ud55c p-\uac12\uc744 \uacc4\uc0b0\ud569\ub2c8\ub2e4.<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\">\uc77c\ubd80 Mahalanobis \uac70\ub9ac\uac00 \ub2e4\ub978 \uac70\ub9ac\ubcf4\ub2e4 \ud6e8\uc52c \ub354 \ud06c\ub2e4\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uac70\ub9ac \uc911 \ud1b5\uacc4\uc801\uc73c\ub85c \uc720\uc758\ubbf8\ud55c \uac83\uc774 \uc788\ub294\uc9c0 \ud655\uc778\ud558\ub824\uba74 \ud574\ub2f9 \uac70\ub9ac\uc758 p-\uac12\uc744 \uacc4\uc0b0\ud574\uc57c \ud569\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\uac01 \uac70\ub9ac\uc5d0 \ub300\ud55c p-\uac12\uc740 k-1 \uc790\uc720\ub3c4\ub97c \uac16\ub294 Mahalanobis \uac70\ub9ac\uc758 \uce74\uc774\uc81c\uacf1 \ud1b5\uacc4\ub7c9\uc5d0 \ud574\ub2f9\ud558\ub294 p-\uac12\uc73c\ub85c \uacc4\uc0b0\ub429\ub2c8\ub2e4. \uc5ec\uae30\uc11c k = \ubcc0\uc218 \uc218\uc785\ub2c8\ub2e4. \ub530\ub77c\uc11c \uc774 \uacbd\uc6b0\uc5d0\ub294 4-1 = 3\uc758 \uc790\uc720\ub3c4\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4.<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #107d3f;\">from<\/span> scipy.stats <span style=\"color: #107d3f;\">import<\/span> chi2\n\n<span style=\"color: #008080;\">#calculate p-value for each mahalanobis distance<\/span> \ndf['p'] = 1 - chi2.cdf(df['mahalanobis'], 3)\n\n<span style=\"color: #008080;\">#display p-values for first five rows in dataframe<\/span>\ndf.head()\n\n score hours prep grade mahalanobis p\n0 91 16 3 70 16.501963 0.000895\n1 93 6 4 88 2.639286 0.450644\n2 72 3 0 80 4.850797 0.183054\n3 87 1 3 83 5.201261 0.157639\n4 86 2 4 88 3.828734 0.280562\n<\/strong><\/pre>\n<p> <span style=\"color: #000000;\">\uc77c\ubc18\uc801\uc73c\ub85c p-\uac12\uc774 <strong>0.001\ubcf4\ub2e4 \uc791\uc73c\uba74<\/strong> \uc774\uc0c1\uac12\uc73c\ub85c \uac04\uc8fc\ub429\ub2c8\ub2e4.<\/span> <span style=\"color: #000000;\">\uccab \ubc88\uc9f8 \uad00\uce21\uac12\uc740 p-\uac12\uc774 0.001\ubcf4\ub2e4 \uc791\uae30 \ub54c\ubb38\uc5d0 \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c \uc774\uc0c1\uac12\uc774\ub77c\ub294 \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n<p> <span style=\"color: #000000;\">\ubb38\uc81c\uc758 \ub9e5\ub77d\uc5d0 \ub530\ub77c \uc774 \uad00\ucc30\uc740 \uc774\uc0c1\uce58\uc774\uace0 \ubd84\uc11d \uacb0\uacfc\uc5d0 \uc601\ud5a5\uc744 \ubbf8\uce60 \uc218 \uc788\uc73c\ubbc0\ub85c \ub370\uc774\ud130 \uc138\ud2b8\uc5d0\uc11c \uc81c\uac70\ud558\uae30\ub85c \uacb0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\ub9c8\ud560\ub77c\ub178\ube44\uc2a4 \uac70\ub9ac\ub294 \ub2e4\ubcc0\ub7c9 \uacf5\uac04\uc5d0\uc11c \ub450 \uc810 \uc0ac\uc774\uc758 \uac70\ub9ac\uc785\ub2c8\ub2e4. \uc774\ub294 \uc5ec\ub7ec \ubcc0\uc218\ub97c \ud3ec\ud568\ud558\ub294 \ud1b5\uacc4 \ubd84\uc11d\uc5d0\uc11c \uc774\uc0c1\uac12\uc744 \ud0d0\uc9c0\ud558\ub294 \ub370 \uc790\uc8fc \uc0ac\uc6a9\ub429\ub2c8\ub2e4. \uc774 \ud29c\ud1a0\ub9ac\uc5bc\uc5d0\uc11c\ub294 Python\uc5d0\uc11c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc744 \uc124\uba85\ud569\ub2c8\ub2e4. \uc608: Python\uc758 Mahalanobis \uac70\ub9ac Python\uc758 \ub370\uc774\ud130\uc138\ud2b8\uc5d0 \uc788\ub294 \uac01 \uad00\uce21\uac12\uc5d0 \ub300\ud55c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub824\uba74 \ub2e4\uc74c \ub2e8\uacc4\ub97c \uc0ac\uc6a9\ud558\uc138\uc694. 1\ub2e8\uacc4: \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub9cc\ub4ed\ub2c8\ub2e4. \uba3c\uc800, 20\uba85\uc758 \ud559\uc0dd\uc758 \uc2dc\ud5d8 \uc810\uc218\uc640 \ud568\uaed8 \uacf5\ubd80\ud55c \uc2dc\uac04, \uce58\ub978 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[20],"tags":[],"class_list":["post-829","post","type-post","status-publish","format-standard","hentry","category-20"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v21.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Python\uc5d0\uc11c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95 \u2013 Statorials\ud559<\/title>\n<meta name=\"description\" content=\"Python\uc5d0\uc11c Mahalanobis \uac70\ub9ac\ub97c \uacc4\uc0b0\ud558\ub294 \ubc29\ubc95\uc5d0 \ub300\ud55c \uac04\ub2e8\ud55c \uc124\uba85\uc785\ub2c8\ub2e4.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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