Maalesef Seaborn’da regresyon denklemini sat\u0131rdan \u00e7\u0131karmaya y\u00f6nelik yerle\u015fik bir \u00f6zellik yoktur, ancak regresyon katsay\u0131lar\u0131n\u0131 h\u0131zl\u0131 bir \u015fekilde bulmak i\u00e7in scipy.stats.linregress<\/strong> i\u015flevini kullanabilirsiniz:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnek, bu s\u00f6zdiziminin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n \u00c7e\u015fitli \u00f6\u011frencilerin \u00e7al\u0131\u015f\u0131lan saatler ve final s\u0131nav puanlar\u0131 hakk\u0131nda bilgi i\u00e7eren a\u015fa\u011f\u0131daki panda DataFrame’e sahip oldu\u011fumuzu varsayal\u0131m:<\/span><\/p>\n Diyelim ki veri noktalar\u0131n\u0131 \u00e7izmek ve verilere uygun bir regresyon \u00e7izgisi eklemek istiyoruz.<\/span><\/p>\n Bunu yapmak i\u00e7in a\u015fa\u011f\u0131daki s\u00f6zdizimini kullanabiliriz:<\/span> <\/p>\n Sonu\u00e7tan regresyon \u00e7izgisinin a\u015fa\u011f\u0131daki denkleme sahip oldu\u011funu g\u00f6rebiliriz:<\/span><\/p>\n y = 77,4 + 1,327<\/strong><\/span><\/p>\n Bu denklemi seaborn regplot’ta<\/strong> g\u00f6r\u00fcnt\u00fclemek istiyorsak matplotlib text()<\/strong> fonksiyonunu kullanabiliriz:<\/span> <\/p>\n Regresyon denkleminin art\u0131k grafi\u011fin sol \u00fcst k\u00f6\u015fesinde g\u00f6r\u00fcnt\u00fclendi\u011fini unutmay\u0131n.<\/span><\/p>\n text()<\/strong> fonksiyonunda regresyon denkleminin (2, 95) (x, y) koordinatlar\u0131ndan g\u00f6r\u00fcnt\u00fclenmesi gerekti\u011fini belirtti\u011fimize dikkat edin.<\/span><\/p>\n Regresyon denklemini kendi \u00e7iziminizde istedi\u011finiz yerde g\u00f6r\u00fcnt\u00fclemek i\u00e7in bu koordinatlar\u0131 de\u011fi\u015ftirmekten \u00e7ekinmeyin.<\/span><\/p>\n Not<\/strong> : Seaborn regplot<\/strong> i\u015flevine ili\u015fkin t\u00fcm belgeleri burada<\/a> bulabilirsiniz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler Seaborn’da di\u011fer genel g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini a\u00e7\u0131klamaktad\u0131r:<\/span><\/p>\n Seaborn grafi\u011finin \u015fekil boyutu nas\u0131l ayarlan\u0131r?<\/a> Bir veri k\u00fcmesine uygun do\u011frusal bir regresyon modeli \u00e7izmek i\u00e7in seaborn regplot i\u015flevini kullanabilirsiniz. Maalesef Seaborn’da regresyon denklemini sat\u0131rdan \u00e7\u0131karmaya y\u00f6nelik yerle\u015fik bir \u00f6zellik yoktur, ancak regresyon katsay\u0131lar\u0131n\u0131 h\u0131zl\u0131 bir \u015fekilde bulmak i\u00e7in scipy.stats.linregress i\u015flevini kullanabilirsiniz: import scipy import seaborn as sns #create regplot p = sns. regplot (data=df, x=df. x , y=df. y ) […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-4236","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n import<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. x<\/span> , y=df. y<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n<\/strong><\/pre>\n \u00d6rnek: Seaborn Regplot’ta regresyon denklemini g\u00f6sterin<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' hours<\/span> ': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n ' score<\/span> ': [77, 79, 84, 80, 81, 89, 95, 90, 83, 89]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n hours score\n0 1 77\n1 2 79\n2 3 84\n3 4 80\n4 5 81\n5 6 89\n6 7 95\n7 8 90\n8 9 83\n9 10 89\n<\/strong><\/pre>\n import<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. hours<\/span> , y=df. score<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n\n#display slope and intercept of regression equation\n<\/span>print<\/span> (intercept, slope)\n\n77.39999999999995 1.3272727272727356\n<\/strong><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/regplot1.jpg\")
<\/p>\n import<\/span> matplotlib. pyplot<\/span> as<\/span> plt<\/span>\nimport<\/span> scipy\nimport<\/span> seaborn as<\/span> sns\n\n#create regplot\n<\/span>p = sns. regplot<\/span> (data=df, x=df. hours<\/span> , y=df. score<\/span> )\n\n#calculate slope and intercept of regression equation\n<\/span>slope, intercept, r, p, sterr = scipy. stats<\/span> . linregress<\/span> (x= p.get_lines<\/span> ()[0] .get_xdata<\/span> (),\n y=p. get_lines<\/span> ()[0]. get_ydata<\/span> ())\n\n#add regression equation to plot\n<\/span>plt. text<\/span> (2, 95, ' y =<\/span> ' + str(round(intercept,3)) + ' +<\/span> ' + str(round(slope,3)) + ' x<\/span> ')<\/span><\/strong> <\/pre>\n![\"Seaborn](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/regplot2.jpg\")
<\/p>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Seaborn’da bir efsanenin konumu nas\u0131l de\u011fi\u015ftirilir?<\/a>
Seaborn grafi\u011findeki eksen etiketleri nas\u0131l de\u011fi\u015ftirilir?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"