<\/p>\n
Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz bir hata:<\/span><\/p>\n Bu hata, Python’da bir regresyon modeli uydurmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ve modeli yerle\u015ftirmeden \u00f6nce kategorik de\u011fi\u015fkenleri kukla de\u011fi\u015fkenlere<\/a> d\u00f6n\u00fc\u015ft\u00fcremedi\u011finizde ortaya \u00e7\u0131kar.<\/span><\/p>\n A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l d\u00fczeltilece\u011fi g\u00f6sterilmektedir.<\/span><\/p>\n A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu varsayal\u0131m DataFrame:<\/span><\/p>\n \u015eimdi, tahmin de\u011fi\u015fkenleri olarak tak\u0131m, asist ve ribaundlar\u0131 ve tepki de\u011fi\u015fkeni<\/a> olarak da puanlar\u0131 kullanarak \u00e7oklu do\u011frusal regresyon modelini<\/a> uydurmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131m\u0131z\u0131 varsayal\u0131m:<\/span><\/p>\n “Tak\u0131m” de\u011fi\u015fkeni kategorik oldu\u011fundan ve regresyon modeline uymadan \u00f6nce onu kukla de\u011fi\u015fkene d\u00f6n\u00fc\u015ft\u00fcrmedi\u011fimizden hata al\u0131yoruz.<\/span><\/p>\n Bu hatay\u0131 d\u00fczeltmenin en kolay yolu “team” de\u011fi\u015fkenini pandas.get_dummies()<\/a> i\u015flevini kullanarak yapay bir de\u011fi\u015fkene d\u00f6n\u00fc\u015ft\u00fcrmektir.<\/span><\/p>\n Not<\/strong> : Regresyon modellerindeki kukla de\u011fi\u015fkenler hakk\u0131nda h\u0131zl\u0131 bir bilgi tazelemesi i\u00e7in bu e\u011fitime<\/a> g\u00f6z at\u0131n.<\/span><\/p>\n A\u015fa\u011f\u0131daki kod “tak\u0131m”\u0131n yapay bir de\u011fi\u015fkene nas\u0131l d\u00f6n\u00fc\u015ft\u00fcr\u00fclece\u011fini g\u00f6sterir:<\/span><\/p>\n \u201cTak\u0131m\u201d s\u00fctunundaki de\u011ferler \u201cA\u201d ve \u201cB\u201dden 0 ve 1\u2019e d\u00f6n\u00fc\u015ft\u00fcr\u00fclm\u00fc\u015ft\u00fcr.<\/span><\/span><\/p>\n Art\u0131k yeni “team_B” de\u011fi\u015fkenini kullanarak \u00e7oklu do\u011frusal regresyon modelini yerle\u015ftirebiliriz:<\/span><\/p>\n Bu sefer regresyon modelini hatas\u0131z bir \u015fekilde s\u0131\u011fd\u0131rabildi\u011fimizi unutmay\u0131n.<\/span><\/p>\n Not<\/strong> : Ols()<\/strong> i\u015flevine ili\u015fkin t\u00fcm belgeleri buradaki<\/a> statsmodels kitapl\u0131\u011f\u0131nda bulabilirsiniz.<\/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 Pandas’ta KeyError Nas\u0131l D\u00fczeltilir<\/a> Python kullan\u0131rken kar\u015f\u0131la\u015fabilece\u011finiz bir hata: ValueError : Pandas data cast to numpy dtype of object. Check input data with np.asarray(data). Bu hata, Python’da bir regresyon modeli uydurmaya \u00e7al\u0131\u015ft\u0131\u011f\u0131n\u0131zda ve modeli yerle\u015ftirmeden \u00f6nce kategorik de\u011fi\u015fkenleri kukla de\u011fi\u015fkenlere d\u00f6n\u00fc\u015ft\u00fcremedi\u011finizde ortaya \u00e7\u0131kar. A\u015fa\u011f\u0131daki \u00f6rnekte bu hatan\u0131n pratikte nas\u0131l d\u00fczeltilece\u011fi g\u00f6sterilmektedir. Hata nas\u0131l yeniden olu\u015fturulur? A\u015fa\u011f\u0131daki pandalara sahip oldu\u011fumuzu […]<\/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-2421","post","type-post","status-publish","format-standard","hentry","category-rehber"],"yoast_head":"\n ValueError<\/span> : Pandas data cast to numpy dtype of object. Check input data with\nnp.asarray(data).\n<\/strong><\/pre>\n Hata nas\u0131l yeniden olu\u015fturulur?<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n ' points<\/span> ': [14, 19, 8, 12, 17, 19, 22, 25]})\n\n#view DataFrame\n<\/span>df\n\n\tteam assists rebounds points\n0 A 5 11 14\n1 To 7 8 19\n2 A 7 10 8\n3 to 9 6 12\n4 B 12 6 17\n5 B 9 5 19\n6 B 9 9 22\n7 B 4 12 25<\/span><\/span><\/strong><\/pre>\n import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df['points']\n\n#define predictor variables\n<\/span>x = df[['team', 'assists', 'rebounds']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#attempt to fit regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\nValueError<\/span> : Pandas data cast to numpy dtype of object. Check input data with\nnp.asarray(data).\n<\/span><\/span><\/strong><\/pre>\n Hata nas\u0131l d\u00fczeltilir?<\/strong><\/span><\/h3>\n
import<\/span> pandas as<\/span> pd\n\n#createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' team<\/span> ': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'],\n ' assists<\/span> ': [5, 7, 7, 9, 12, 9, 9, 4],\n ' rebounds<\/span> ': [11, 8, 10, 6, 6, 5, 9, 12],\n ' points<\/span> ': [14, 19, 8, 12, 17, 19, 22, 25]})\n\n#convert \"team\" to dummy variable\n<\/span>df = pd. get_dummies<\/span> (df, columns=[' team<\/span> '], drop_first= True<\/span> )\n\n#view updated DataFrame\n<\/span>df\n\n assists rebounds points team_B\n0 5 11 14 0\n1 7 8 19 0\n2 7 10 8 0\n3 9 6 12 0\n4 12 6 17 1\n5 9 5 19 1\n6 9 9 22 1\n7 4 12 25 1<\/span><\/span><\/strong><\/pre>\n import<\/span> statsmodels. api<\/span> as<\/span> sm\n\n#define response variable\n<\/span>y = df['points']\n\n#define predictor variables\n<\/span>x = df[['team_B', 'assists', 'rebounds']]\n\n#add constant to predictor variables\n<\/span>x = sm. add_constant<\/span> (x)\n\n#fit regression model\n<\/span>model = sm. OLS<\/span> (y,x). fit<\/span> ()\n\n#view summary of model fit\n<\/span>print<\/span> ( model.summary<\/span> ())\n\n OLS Regression Results \n==================================================== ============================\nDept. Variable: R-squared points: 0.701\nModel: OLS Adj. R-squared: 0.476\nMethod: Least Squares F-statistic: 3.119\nDate: Thu, 11 Nov 2021 Prob (F-statistic): 0.150\nTime: 14:49:53 Log-Likelihood: -19.637\nNo. Observations: 8 AIC: 47.27\nDf Residuals: 4 BIC: 47.59\nDf Model: 3 \nCovariance Type: non-robust \n==================================================== ============================\n coef std err t P>|t| [0.025 0.975]\n-------------------------------------------------- ----------------------------\nconst 27.1891 17.058 1.594 0.186 -20.171 74.549\nteam_B 9.1288 3.032 3.010 0.040 0.709 17.548\nassists -1.3445 1.148 -1.171 0.307 -4.532 1.843\nrebounds -0.5174 1.099 -0.471 0.662 -3.569 2.534\n==================================================== ============================\nOmnibus: 0.691 Durbin-Watson: 3.075\nProb(Omnibus): 0.708 Jarque-Bera (JB): 0.145\nSkew: 0.294 Prob(JB): 0.930\nKurtosis: 2.698 Cond. No. 140.\n==================================================== ============================\n<\/span><\/span><\/strong><\/pre>\n Ek kaynaklar<\/strong><\/span><\/h3>\n
Nas\u0131l d\u00fczeltilir? ValueError: float NaN int’ye d\u00f6n\u00fc\u015ft\u00fcr\u00fclemiyor<\/a>
Nas\u0131l d\u00fczeltilir? ValueError: \u0130\u015flenenler \u015fekillerle yay\u0131nlanamad\u0131<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"