<\/p>\n
Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz yayg\u0131n bir hata:<\/span><\/p>\n Bu hata genellikle scikit-learn mod\u00fcl\u00fcnden bir fonksiyonu kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar ancak giri\u015f olarak kulland\u0131\u011f\u0131n\u0131z DataFrame veya matrisin NaN de\u011ferleri veya sonsuz de\u011ferleri vard\u0131r.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l \u00e7\u00f6z\u00fclece\u011fi g\u00f6sterilmektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n \u015eimdi scikit-learn<\/a> fonksiyonlar\u0131n\u0131 kullanarak \u00e7oklu do\u011frusal regresyon modeline<\/a> uymaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n Kulland\u0131\u011f\u0131m\u0131z DataFrame’in hem sonsuz hem de NaN de\u011ferleri olmas\u0131ndan dolay\u0131 hata al\u0131yoruz.<\/span><\/p>\n Bu hatay\u0131 \u00e7\u00f6zmenin yolu \u00f6ncelikle sonsuz veya NaN de\u011ferleri i\u00e7eren t\u00fcm sat\u0131rlar\u0131 DataFrame’den kald\u0131rmakt\u0131r:<\/span><\/p>\n Sonsuz veya NaN de\u011ferine sahip iki sat\u0131r kald\u0131r\u0131ld\u0131.<\/span><\/p>\n Art\u0131k do\u011frusal regresyon modelimize uymaya devam edebiliriz:<\/span><\/p>\n \u00d6ncelikle DataFrame’den sonsuz veya NaN de\u011ferli sat\u0131rlar\u0131 kald\u0131rd\u0131\u011f\u0131m\u0131z i\u00e7in bu sefer herhangi bir hata almad\u0131\u011f\u0131m\u0131z\u0131 unutmay\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki e\u011fitimlerde Python’daki di\u011fer yayg\u0131n hatalar\u0131n nas\u0131l d\u00fczeltilece\u011fi a\u00e7\u0131klanmaktad\u0131r:<\/span><\/p>\n Python’da Nas\u0131l D\u00fczeltilir: ‘numpy.ndarray’ nesnesi \u00e7a\u011fr\u0131labilir de\u011fil<\/a> Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz yayg\u0131n bir hata: ValueError: Input contains infinity or a value too large for dtype(‘float64’). Bu hata genellikle scikit-learn mod\u00fcl\u00fcnden bir fonksiyonu kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ortaya \u00e7\u0131kar ancak giri\u015f olarak kulland\u0131\u011f\u0131n\u0131z DataFrame veya matrisin NaN de\u011ferleri veya sonsuz de\u011ferleri vard\u0131r. A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l \u00e7\u00f6z\u00fclece\u011fi g\u00f6sterilmektedir. Hata nas\u0131l yeniden olu\u015fturulur? A\u015fa\u011f\u0131daki pandalara […]<\/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-3558","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n ValueError: Input contains infinity or a value too large for dtype('float64').\n<\/strong><\/span><\/pre>\n Hata nas\u0131l yeniden olu\u015fturulur?<\/strong><\/span><\/h2>\n
import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' x1<\/span> ': [1, 2, 2, 4, 2, 1, 5, 4, 2, 4, 4],\n ' x2<\/span> ': [1, 3, 3, 5, 2, 2, 1, np.inf, 0, 3, 4],\n ' y<\/span> ': [np.nan, 78, 85, 88, 72, 69, 94, 94, 88, 92, 90]})\n\n#view DataFrame\n<\/span>print<\/span> (df)\n\n x1 x2 y\n0 1 1.0 NaN\n1 2 3.0 78.0\n2 2 3.0 85.0\n3 4 5.0 88.0\n4 2 2.0 72.0\n5 1 2.0 69.0\n6 5 1.0 94.0\n7 4 lower 94.0\n8 2 0.0 88.0\n9 4 3.0 92.0\n10 4 4.0 90.0<\/span><\/span><\/strong><\/pre>\n from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df[[' x1<\/span> ', ' x2<\/span> ']], df. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n\n#print model intercept and coefficients\n<\/span>print<\/span> (model. intercept_<\/span> , model. coef_<\/span> )\n\n<\/span>ValueError: Input contains infinity or a value too large for dtype('float64').<\/span>\n<\/span><\/strong><\/pre>\n Hata nas\u0131l d\u00fczeltilir?<\/strong><\/span><\/h2>\n
#remove rows with any values that are not finite\n<\/span>df_new = df[np. isfinite<\/span> (df). all<\/span> ( 1<\/span> )]\n\n#view updated DataFrame\n<\/span>print<\/span> (df_new)\n\n x1 x2 y\n1 2 3.0 78.0\n2 2 3.0 85.0\n3 4 5.0 88.0\n4 2 2.0 72.0\n5 1 2.0 69.0\n6 5 1.0 94.0\n8 2 0.0 88.0\n9 4 3.0 92.0\n10 4 4.0 90.0\n<\/span><\/span><\/strong><\/pre>\n from<\/span> sklearn. linear_model<\/span> import<\/span> LinearRegression\n\n#initiate linear regression model\n<\/span>model = LinearRegression()\n\n#define predictor and response variables\n<\/span>x, y = df_new[[' x1<\/span> ', ' x2<\/span> ']], df_new. y<\/span>\n\n#fit regression model\n<\/span>model. fit<\/span> (x,y)\n\n#print model intercept and coefficients\n<\/span>print<\/span> (model. intercept_<\/span> , model. coef_<\/span> )\n\n69.85144124168515 [5.72727273 -0.93791574]\n<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Nas\u0131l d\u00fczeltilir: TypeError: ‘numpy.float64’ nesnesi \u00e7a\u011fr\u0131labilir de\u011fil<\/a>
Nas\u0131l Onar\u0131l\u0131r: T\u00fcr Hatas\u0131: Beklenen Dize veya Bayt Nesnesi<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"