<\/p>\n
Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz bir hata:<\/span><\/p>\n Bu hata genellikle a\u015fa\u011f\u0131daki sat\u0131r\u0131 kullanarak train_test_split<\/strong> i\u015flevini sklearn’den<\/strong> i\u00e7e aktarmaya \u00e7al\u0131\u015f\u0131rken ortaya \u00e7\u0131kar:<\/span><\/p>\n Ancak cross_validation<\/strong> alt mod\u00fcl\u00fcn\u00fcn yerini model_selection<\/strong> alt mod\u00fcl\u00fc alm\u0131\u015ft\u0131r, dolay\u0131s\u0131yla a\u015fa\u011f\u0131daki sat\u0131r\u0131 kullanman\u0131z gerekir:<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l \u00e7\u00f6z\u00fclece\u011fi g\u00f6sterilmektedir.<\/span><\/p>\n Diyelim ki bir pandan\u0131n DataFrame’ini e\u011fitim ve test setlerine b\u00f6lmek i\u00e7in sklearn’in<\/strong> train_test_split<\/strong> fonksiyonunu kullanmak istiyoruz.<\/span><\/p>\n train_test_split<\/strong> i\u015flevini i\u00e7e aktarmak i\u00e7in a\u015fa\u011f\u0131daki kodu kullanmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n train_test_split<\/strong> i\u015flevini i\u00e7e aktarmaya \u00e7al\u0131\u015f\u0131rken yanl\u0131\u015f alt mod\u00fcl ad\u0131n\u0131 kulland\u0131\u011f\u0131m\u0131z i\u00e7in bir hata al\u0131yoruz.<\/span><\/p>\n Bu hatay\u0131 d\u00fczeltmek i\u00e7in bunun yerine model_selection<\/strong> alt mod\u00fcl\u00fcn\u00fc kullanmam\u0131z yeterlidir:<\/span><\/p>\n Bu sefer herhangi bir hata alm\u0131yoruz.<\/span><\/p>\n Daha sonra bir pandan\u0131n DataFrame’ini bir e\u011fitim ve test setine b\u00f6lmek i\u00e7in train_test_split<\/strong> fonksiyonunu kullanabiliriz:<\/span><\/p>\n train_test_split<\/strong> fonksiyonunu hatas\u0131z ve ba\u015far\u0131l\u0131 bir \u015fekilde kullanmay\u0131 ba\u015far\u0131yoruz.<\/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 Nas\u0131l d\u00fczeltilir? S\u00fctunlar \u00e7ak\u0131\u015f\u0131yor ancak son ek belirtilmemi\u015f<\/a> Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz bir hata: ModuleNotFoundError : No module named ‘sklearn.cross_validation’ Bu hata genellikle a\u015fa\u011f\u0131daki sat\u0131r\u0131 kullanarak train_test_split i\u015flevini sklearn’den i\u00e7e aktarmaya \u00e7al\u0131\u015f\u0131rken ortaya \u00e7\u0131kar: from sklearn. cross_validation import train_test_split Ancak cross_validation alt mod\u00fcl\u00fcn\u00fcn yerini model_selection alt mod\u00fcl\u00fc alm\u0131\u015ft\u0131r, dolay\u0131s\u0131yla a\u015fa\u011f\u0131daki sat\u0131r\u0131 kullanman\u0131z gerekir: from sklearn. model_selection import train_test_split A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l […]<\/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-3542","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n ModuleNotFoundError : No module named 'sklearn.cross_validation'\n<\/span><\/span><\/strong><\/pre>\n from<\/span> sklearn. cross_validation<\/span> import<\/span> train_test_split<\/strong><\/pre>\n from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split<\/strong><\/pre>\n Hata nas\u0131l yeniden olu\u015fturulur?<\/strong><\/span><\/h2>\n
from<\/span> sklearn. cross_validation<\/span> import<\/span> train_test_split\n\nModuleNotFoundError : No module named 'sklearn.cross_validation'<\/span><\/span>\n<\/strong><\/pre>\n Hata nas\u0131l d\u00fczeltilir?<\/strong><\/span><\/h2>\n
from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\n<\/strong><\/pre>\n from<\/span> sklearn. model_selection<\/span> import<\/span> train_test_split\nimport<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\n\n#make this example reproducible\n<\/span>n.p. random<\/span> . seeds<\/span> (1)\n\n#create DataFrame with 1000 rows and 3 columns\n<\/span>df = pd. DataFrame<\/span> ({' x1<\/span> ': np.random.randint(30, size=1000),\n ' x2<\/span> ': np.random.randint(12, size=1000),\n ' y<\/span> ': np.random.randint(2, size=1000)})\n\n#split original DataFrame into training and testing sets\n<\/span>train, test = train_test_split(df, test_size=0.2, random_state=0)\n\n#view first few rows of each set\n<\/span>print<\/span> ( train.head<\/span> ())\n\n x1 x2 y\n687 16 2 0\n500 18 2 1\n332 4 10 1\n979 2 8 1\n817 11 1 0\n\nprint<\/span> ( test.head<\/span> ())\n\n x1 x2 y\n993 22 1 1\n859 27 6 0\n298 27 8 1\n553 20 6 0\n672 9 2 1<\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h2>\n
Nas\u0131l d\u00fczeltilir: ‘numpy.ndarray’ nesnesinin ‘append’ \u00f6zelli\u011fi yok<\/a>
Nas\u0131l d\u00fczeltilir? T\u00fcm skaler de\u011ferleri kullan\u0131yorsan\u0131z bir dizin iletmeniz gerekir<\/a>
Nas\u0131l d\u00fczeltilir? ValueError: float NaN int’ye d\u00f6n\u00fc\u015ft\u00fcr\u00fclemiyor<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"