En k\u00fc\u00e7\u00fck kareler y\u00f6ntemi,<\/strong> belirli bir veri k\u00fcmesine en iyi uyan regresyon do\u011frusunu bulmak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir.<\/span><\/p>\n En k\u00fc\u00e7\u00fck kareler ayarlamas\u0131n\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in NumPy’deki linalg.lstsq(<\/strong> ) fonksiyonunu kullanabiliriz.<\/span><\/p>\n A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, bu i\u015flevin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir.<\/span><\/p>\n A\u015fa\u011f\u0131daki NumPy dizilerini olu\u015fturarak ba\u015flayal\u0131m:<\/span><\/p>\n En k\u00fc\u00e7\u00fck kareler uyumunu ger\u00e7ekle\u015ftirmek ve verilere en iyi “uyan” sat\u0131r\u0131 bulmak i\u00e7in a\u015fa\u011f\u0131daki kodu kullanabiliriz:<\/span><\/p>\n Sonu\u00e7, en uygun \u00e7izginin e\u011fim<\/strong> ve kesi\u015fme<\/strong> de\u011ferlerini i\u00e7eren bir tablodur.<\/span><\/span><\/p>\n Sonu\u00e7tan \u015funu g\u00f6rebiliriz:<\/span><\/p>\n Bu iki de\u011feri kullanarak en uygun \u00e7izginin denklemini yazabiliriz:<\/span><\/p>\n \u0177 = 7,767 + 0,969x<\/span><\/p>\n En iyi uyum \u00e7izgisini nas\u0131l yorumlayaca\u011f\u0131n\u0131z a\u015fa\u011f\u0131da a\u00e7\u0131klanm\u0131\u015ft\u0131r:<\/span><\/p>\n Ayr\u0131ca x’in de\u011ferine ba\u011fl\u0131 olarak y’nin de\u011ferini tahmin etmek i\u00e7in en iyi uyum \u00e7izgisini de kullanabiliriz.<\/span><\/p>\n \u00d6rne\u011fin, xa’n\u0131n de\u011feri 10 ise y’nin de\u011ferinin 17,457<\/strong> olaca\u011f\u0131n\u0131 tahmin ederiz:<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimler NumPy’de di\u011fer genel g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini a\u00e7\u0131klamaktad\u0131r:<\/span><\/p>\n NumPy dizisinden belirli \u00f6\u011feler nas\u0131l kald\u0131r\u0131l\u0131r<\/a> En k\u00fc\u00e7\u00fck kareler y\u00f6ntemi, belirli bir veri k\u00fcmesine en iyi uyan regresyon do\u011frusunu bulmak i\u00e7in kullanabilece\u011fimiz bir y\u00f6ntemdir. En k\u00fc\u00e7\u00fck kareler ayarlamas\u0131n\u0131 ger\u00e7ekle\u015ftirmek i\u00e7in NumPy’deki linalg.lstsq( ) fonksiyonunu kullanabiliriz. A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, bu i\u015flevin pratikte nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir. Ad\u0131m 1: X ve Y de\u011ferlerini girin A\u015fa\u011f\u0131daki NumPy dizilerini olu\u015fturarak ba\u015flayal\u0131m: import numpy as np […]<\/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-3514","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n Ad\u0131m 1: X ve Y de\u011ferlerini girin<\/strong><\/span><\/h2>\n
import<\/span> numpy as<\/span> np\n\n#define x and y arrays\n<\/span>x = np. array<\/span> ([6, 7, 7, 8, 12, 14, 15, 16, 16, 19])\n\ny = np. array<\/span> ([14, 15, 15, 17, 18, 18, 19, 24, 25, 29])\n<\/strong><\/pre>\n 2. Ad\u0131m: En k\u00fc\u00e7\u00fck kareler ayar\u0131n\u0131 ger\u00e7ekle\u015ftirin<\/strong><\/span><\/h2>\n
#perform least squares fitting\n<\/span>n.p. linalg<\/span> . lstsq<\/span> (np. vstack<\/span> ([x, np. ones<\/span> ( len<\/span> (x))]). T<\/span> , y, rcond= None<\/span> )[ 0<\/span> ]\n\narray([0.96938776, 7.76734694])\n<\/strong><\/pre>\n\n
3. Ad\u0131m: Sonu\u00e7lar\u0131 yorumlay\u0131n<\/strong><\/span><\/h2>\n
\n
\n
Ek kaynaklar<\/strong><\/span><\/h2>\n
NumPy dizisinde maksimum de\u011fer indeksi nas\u0131l elde edilir<\/a>
NumPy dizisini de\u011ferlerle doldurma<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"