Bu t\u00fcr bir regresyon \u015fu \u015fekli al\u0131r:<\/span><\/p>\n Y = \u03b2 0<\/sub> +<\/sup> \u03b2 1<\/sub> X + \u03b2 2<\/sub> X 2<\/sup> + \u2026 + \u03b2 h<\/sub><\/span><\/p>\n burada h<\/em> polinomun \u201cderecesidir\u201d.<\/span><\/p>\n A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, Python’da sklearn kullan\u0131larak polinom regresyonunun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini g\u00f6sterir.<\/span><\/p>\n \u00d6ncelikle bir tahmincinin ve bir yan\u0131t de\u011fi\u015fkeninin de\u011ferlerini tutacak iki NumPy dizisi olu\u015ftural\u0131m:<\/span> <\/p>\n Da\u011f\u0131l\u0131m grafi\u011finden x ve y aras\u0131ndaki ili\u015fkinin do\u011frusal olmad\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n Bu nedenle, iki de\u011fi\u015fken aras\u0131ndaki do\u011frusal olmayan ili\u015fkiyi yakalamak i\u00e7in verilere bir polinom regresyon modeli uydurmak iyi bir fikirdir.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod, bu veri k\u00fcmesine 3. derece polinom regresyon modelini s\u0131\u011fd\u0131rmak i\u00e7in sklearn i\u015flevlerinin nas\u0131l kullan\u0131laca\u011f\u0131n\u0131 g\u00f6sterir:<\/span><\/p>\n Son sat\u0131rda g\u00f6sterilen model katsay\u0131lar\u0131n\u0131 kullanarak uygun polinom regresyon denklemini a\u015fa\u011f\u0131daki gibi yazabiliriz:<\/span><\/p>\n y = -0,109x 3<\/sup> + 2,256x 2<\/sup> \u2013 11,839x + 33,626<\/span><\/p>\n Bu denklem, tahmin edilen de\u011fi\u015fkenin belirli bir de\u011feri verildi\u011finde yan\u0131t de\u011fi\u015fkeninin beklenen de\u011ferini bulmak i\u00e7in kullan\u0131labilir.<\/span><\/p>\n \u00d6rne\u011fin, e\u011fer x 4 ise yan\u0131t de\u011fi\u015fkeni y i\u00e7in beklenen de\u011fer 15,39 olacakt\u0131r:<\/span><\/p>\n y = -0,109(4) 3<\/sup> + 2,256(4) 2<\/sup> \u2013 11,839(4) + 33,626= 15,39<\/span><\/p>\n Not<\/strong> : Bir polinom regresyon modelini farkl\u0131 bir dereceye s\u0131\u011fd\u0131rmak i\u00e7in, PolynomialFeatures()<\/strong> i\u015flevindeki derece<\/strong> ba\u011f\u0131ms\u0131z de\u011fi\u015fkeninin de\u011ferini de\u011fi\u015ftirmeniz yeterlidir.<\/span><\/p>\n Son olarak, orijinal veri noktalar\u0131na uyan polinom regresyon modelini g\u00f6rselle\u015ftirmek i\u00e7in basit bir grafik olu\u015fturabiliriz:<\/span><\/span> <\/p>\n Grafikten, polinom regresyon modelinin verilere a\u015f\u0131r\u0131 uyum<\/a> sa\u011flamadan iyi uyum sa\u011flad\u0131\u011f\u0131n\u0131 g\u00f6rebiliriz.<\/span><\/p>\n Not<\/strong> : Sklearn PolynomialFeatures()<\/strong> i\u015flevine ili\u015fkin t\u00fcm belgeleri burada<\/a> bulabilirsiniz.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde sklearn kullan\u0131larak di\u011fer genel g\u00f6revlerin nas\u0131l ger\u00e7ekle\u015ftirilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Sklearn’den regresyon katsay\u0131lar\u0131 nas\u0131l \u00e7\u0131kar\u0131l\u0131r<\/a> Polinom regresyon, yorday\u0131c\u0131 de\u011fi\u015fken ile yan\u0131t de\u011fi\u015fkeni aras\u0131ndaki ili\u015fkinin do\u011frusal olmad\u0131\u011f\u0131 durumlarda kullanabilece\u011fimiz bir tekniktir. Bu t\u00fcr bir regresyon \u015fu \u015fekli al\u0131r: Y = \u03b2 0 + \u03b2 1 X + \u03b2 2 X 2 + \u2026 + \u03b2 h burada h polinomun \u201cderecesidir\u201d. A\u015fa\u011f\u0131daki ad\u0131m ad\u0131m \u00f6rnek, Python’da sklearn kullan\u0131larak polinom regresyonunun nas\u0131l ger\u00e7ekle\u015ftirilece\u011fini […]<\/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-4060","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n 1. Ad\u0131m: Verileri olu\u015fturun<\/strong><\/span><\/h2>\n
import<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nimport<\/span> numpy as<\/span> np\n\n#define predictor and response variables\n<\/span>x = np. array<\/span> ([2, 3, 4, 5, 6, 7, 7, 8, 9, 11, 12])\ny = np. array<\/span> ([18, 16, 15, 17, 20, 23, 25, 28, 31, 30, 29])\n\n#create scatterplot to visualize relationship between x and y\n<\/span>plt. scatter<\/span> (x,y)\n<\/strong><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polysk1.png\")
<\/p>\n Ad\u0131m 2: Polinom regresyon modelini yerle\u015ftirin<\/strong><\/span><\/h2>\n
from<\/span> sklearn. preprocessing<\/span> import<\/span> PolynomialFeatures\nfrom<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#specify degree of 3 for polynomial regression model\n#include bias=False means don't force y-intercept to equal zero<\/span>\npoly = PolynomialFeatures(degree= 3<\/span> , include_bias= False<\/span> )\n\n#reshape data to work properly with sklearn\n<\/span>poly_features = poly. fit_transform<\/span> ( x.reshape<\/span> (-1, 1))\n\n#fit polynomial regression model\n<\/span>poly_reg_model = LinearRegression()\npoly_reg_model. fit<\/span> (poly_features,y)\n\n#display model coefficients\n<\/span>print<\/span> (poly_reg_model. intercept_<\/span> , poly_reg_model. coef_<\/span> )\n\n33.62640037532282 [-11.83877127 2.25592957 -0.10889554]\n<\/strong><\/span><\/pre>\n Ad\u0131m 3: Polinom regresyon modelini g\u00f6rselle\u015ftirin<\/span><\/strong><\/h2>\n
#use model to make predictions on response variable\n<\/span>y_predicted = poly_reg_model. predict<\/span> (poly_features)\n\n#create scatterplot of x vs. y\n<\/span>plt. scatter<\/span> (x,y)\n\n#add line to show fitted polynomial regression model\n<\/span>plt. plot<\/span> (x,y_predicted,color=' purple<\/span> ')\n<\/strong><\/span><\/pre>\n![\"\"](\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/polysk2.png\")
<\/p>\n Ek kaynaklar<\/span><\/strong><\/span><\/h2>\n
Sklearn kullanarak dengeli hassasiyet nas\u0131l hesaplan\u0131r<\/a>
Sklearn’de s\u0131n\u0131fland\u0131rma raporu nas\u0131l yorumlan\u0131r?<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"